Posted by infotrellislauren on Monday, Dec 15, 2014 @ 1:34 PM
© Phil Date | Dreamstime Stock Photos
© Phil Date | Dreamstime Stock Photos
© Phil Date | Dreamstime Stock Photos

There’s a metaphor I like to use about public washrooms. Have you ever been in a public washroom where the toilet flushes automatically, the soap dispenses automatically, and the water turns on and off automatically, but then the drier is manual, and it seems really jarring and weird because you stick your hands under it expecting it to be automatic too and then nothing happens? That’s what’s going to happen to digital customer experiences and marketing best practices.

Let me elaborate.

Say it’s around the second week of December. I’m working on doing my Christmas shopping still, like many people are at this time of year. I open up an email from a large bookstore chain that I happen to have a loyalty card with – one of the few I actually use and carry around with me, and tolerate the promotional emails from. In the email is an offer that says “Got friends around the world? Check out with this coupon and we’ll ship to three different locations for free when you spend more than $100!”

For me, I’d be thinking: “Holy smokes, that’s perfect!! I have lots of friends around the world! I would love to be able to ship to three different places in one purchase! That’s so convenient!”

That might not be something that would excite you, but that’s why (although I’m not aware of it) I got this email and you didn’t. It’s tailored specifically to me because they know this is an extremely relevant offer that will motivate me to make a large purchase.

So I click to get the coupon and it takes me to a “gift suggestion” page. And somehow, it’s only showing me gifts and books that my friends and my family would like. It’s got science humour books, nerdy video game related books, and even suggests a book with big glossy pictures of cars for the two people on my list of ten loved ones that really dig cars. Me personally, I don’t like cars that much – but this isn’t a list tailored to me anymore, it’s tailored to the people I most care about and would likely spend more money on a gift for.

So here I am, sitting at my computer and thinking “WOW that is perfect for this person I care about, this one here is perfect for THAT person I care about, look at this I’m going to get all my shopping done in one afternoon,” and before I know it I have $250 of things in my basket.

It’s like a next-best-offer section, but super intelligently suggested.

How do they do that? They match my customer profile to my social media profiles, and they not only profile me, but they determine which of my friends I pay the most attention to and then they profile those friends. My activity and relationships on Facebook, Twitter, LinkedIn and other websites will all tell them who I most value of my friends. They can then match those friends of mine to their own internal customer records and provide me with the “next best offer” that would most apply to my friends based on their purchase history, without actually revealing that purchase history to me. If they don’t have a customer profile with the bookstore, they still have lots of data about their likes and interests from their social profiles that build a comprehensive idea of what kinds of books and other items they’d enjoy as gifts.

Now, this is the point that you think “That sounds kind of creepy.”

Yes! Extremely creepy!

But useful to the consumer.

Which is why you would label the suggestions “What’s hot right now!” The shopper can only assume the rest of the world has the same taste as all their friends, which isn’t that big of a stretch if they have a wide variety of relationships with a wide variety of people. By knowing when to make the personalization obvious and when to be more subtle about it, you reduce the chance of making your customers uncomfortable.

Ultimately, the consumer benefits because they get all the stuff they want and they don’t have to wade through products that are irrelevant to them, and the coupons or incentives they’re offered are always relevant or useful. It’s about making life easier for people. “If you could use magic to make shopping better in ways you don’t believe are actually possible, what would you change / improve?”

Now of course, it’s arguable that improving your marketing relevance is less about making it easier for people and more about making it easier to target consumers to spend more money. It really ought to be both, ideally. When the consumer benefits, the seller benefits – the idea being that if you give people a better experience, they reward you with loyalty.

So yes, your end goal is money – you are a business – but at the same time, you differentiate yourself from other businesses by recognizing that every person is unique, and giving them “special treatment” by using technology capable of instantly customizing the experience.

Eventually the majority of companies will be capable of never, ever sending you something that is irrelevant to you. And then when that does happen, your reaction is likely to be, “Wow company, get it together.” To return to the bathroom metaphor, you’ve gone from three automated interactions to one unexpectedly manual one. Before, you’d never have thought about the dissonance, because you’d never been given a different experience. But once the ball gets rolling and you’ve gotten used to it, the experiences you thought of as normal will be bizarrely outmoded and stand out.

So the next time you’re pushing through a crowded mall or clicking through an online catalog trying to figure out what the heck to get for the people on your Christmas list, stop to imagine a better world in which the retailer has suggestions for you that are genuinely helpful and designed for you and the people you want to see smile this holiday season.

Big Data technology is actually making this kind of automated and sophisticated microsegmentation possible. Maybe you’ll even see it in action this time next year.

InfoTrellis is founded by a team of three architects who have been shaping the Information Management market space since 1999. At InfoTrellis we work on developing cutting edge technologies designed to tackle the new challenges facing the modern data-driven company, driving the creation of next-generation products for enabling targeted marketing, highly customized and individualized loyalty programs, deeply detailed competitive analysis, and enriched, automatically updating customer profiling. To learn more about InfoTrellis, visit our website at www.infotrellis.com or contact us directly at info@infotrellis.com

Topics: Big Data Customer Relationship Management Marketing Retail segmentation

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Posted by infotrellislauren on Monday, Nov 24, 2014 @ 10:00 AM

“Recent research by McKinsey and the Massachusetts Institute of Technology shows that companies that inject big data and analytics into their operations outperform their peers by 5% in productivity and 6% in profitability. Our experience suggests that for retail and CPG companies, the upside is at least as great, if not greater.”

Peter Breuer, director of McKinsey & Co.’s retail practice in Germany

With November half over and 2015 starting to peek at us over the horizon, we decided it was time to take a look at a few examples of what retailers have been using big data for in 2014. Here are three examples of use cases for Big Data in retail that have emerged in the last year, followed by a few InfoTrellis predictions about what will happen next in the new year when it comes to the evolution of how companies are implementing their Big Data strategies.

Macy’s

Personalized Marketing

 

The main goal for Macy’s CEO, Terry Lundgren, is to offer more localized, personalized and smarter retail customer experience across all channels. They use Big Data among others to create customer-centric assortments. They analyse a large amount of different data points, such as out-of-stock rates, price promotions, sell-through rates etc. and combine these with SKU data from a product at a certain location and time as well as customer data in order to optimize their local assortments to the individual customer segments in those locations.

 In addition to that, Macy’s gathers, and of course analyses, a vast amount of customer data ranging from visit frequencies and sales to style preferences and online & offline personal motivations. They use this data to create a personalized customer experience including customized incentives at checkouts. Even more, they are now capable of sending hyper-targeted direct mailings to their customers, including 500,000 unique versions of a single mailing. The results are compelling; Macy’s e-commerce division alone has witnessed a growth of over 10% and an overall annual revenue growth of 4% with the use of Big Data Analytics.

(Macy’s Is Changing The Shopping Experience With Big Data Analytics)

Personalizing the user experience is a ubiquitous use case for big data, so it’s exciting to see a retailer actually implementing the technology to accomplish and prove out the value of this marketing strategy. Four percent growth is nothing to scoff at; this number represents millions of dollars on pure profit they didn’t have before. For companies that still believe they can accurately segment their hundreds of thousands of customers with fewer than a hundred profile archetypes, this is an undeniable piece of proof that they may need to consider getting on the bandwagon if they don’t want those millions of dollars to be coming out of their share of the customer’s spending habits.

What’s more, this is a front-and-center application of big data that is highly visible to the shopper. Whether or not they can articulate the difference in quality of experience they get from a retailer that uses it and a retailer that doesn’t, it’s a difference they can intuitively feel and will definitely react to by rewarding one store with loyalty over the others.

Once companies start pulling social data and combining it with internal customer data, their targeting and micro-segmentation capabilities will enable even more uniquely tailored marketing and customer experiences. So long as companies remember that the purpose of this data-collection is to minimize friction and irrelevant messaging for their customers and never to manipulate them or milk them for money like a mindless herd, the consumer stands only to benefit from the evolution of this practice.

For this reason, my prediction is that this will be a big differentiator in the coming years as the companies that experiment with it first (i.e. the early adopters) get better and better, making the gap increasingly noticeable to the end consumer. There will be a scramble by the companies that lagged behind to try to catch up, and this will represent a big shift for the retail industry’s established best practices in much the same way the idea of the loyalty program and the digital storefront did.

LUSH

Supply Chain Efficiencies

 

LUSH Fresh Handmade Cosmetics used big data technology in 2014 to drive in-store profitability and deliver savings of over £1 million in stock loss. Working with many datasets – retail data within EPOS systems, supply chain and stock management, payroll and timesheet systems for staff management – LUSH sought and implemented a technology platform that could be used by employees at every level throughout the business to provide access to relevant sales, stock, store and staff information to improve performance.

 The BI tool is deployed across the entire LUSH organisation. The retail accounts team uses their new platform to dig into the numbers behind the sales in each shop, to keep an eye on ledgers, petty cash and every other aspect of running the business financially.

The technology is used in all the stores, so all the shop managers and employees on the shopfloor have access to updates every hour. The big data technology is also used in LUSH’s manufacturing department for stock management, allowing the team to facilitate orders between the factory and the shops and can keep an eye on stock position around the country. The gathering and analysis of big data has also helped LUSH to get a view of its sales and stock which has led to improvements in forecasting and sourcing of key ingredients from suppliers.

(LUSH Fresh Handmade Cosmetics saves £1 million with user-driven BI)

Big Data doesn’t just have to mean customer-obsessed data like social media or purchase data, and LUSH has demonstrated excellently how better managing and understanding the “big data” generated by business processes can lead to the increased efficiencies that save companies real money. The above quote doesn’t go into a great amount of detail about this, but LUSH is also apparently giving their in-store staff the power to make decisions about the physical customer experience around displays and promotions using insights gained from this data.

The big advantage here is the immediacy of the data and the accessibility of it across the organization. Giving employees in the store both the ability and the direction needed to make swift data-driven changes means a real-time responsiveness that is key to taking advantage of interesting correlations like how shopping behavior changes during certain weather or changing a marketing strategy on the fly when regional tweaks have the ability to boost sales.

The more data they have and the closer they get to real-time, automated analysis, the better companies like LUSH get at managing their backend operations. This isn’t something that the customer will see as clearly as a customized marketing experience, but when a store always magically has the kinds of things they’re hoping to buy and then a few other things they didn’t realize they wanted until they laid eyes on them, repeat visits are likely just for the convenience of the inventory. Customers don’t much care about whether a store loses money on excess, unwanted inventory, but they’re unknowingly benefiting just as much as the company is when predictive analytics can prevent that unfortunate occurrence.

Going forward, these kinds of clever algorithms stand to get better and better as companies pull in data from outside of their company, using social media to understand demand and competition as well as more granular breakdowns by region and even individual store location. I don’t anticipate the rush to get in on this use of Big Data technology will quite as dramatic as need to invest in its marketing applications over the next few years, but it’s a tangible, valuable use-case that will apply to the more pragmatic of executives. We will likely see slow but steady growth in the number of companies that decide to implement Big Data technology with the goal of improving their BI behind the scenes.

Starbucks

Maximized Profitability of Store Locations

Understanding the pools of information pouring into the databases of Starbucks has become a major focus at the international brew chain, even though its high-profile CEO doesn’t much care for it. “Howard [Schultz] doesn’t care about data. He has absolutely no head for data,” said Joe LaCugna, director of analytics and business intelligence at Starbucks during a session at the Big Data Retail Forum in Chicago.

 A full quarter of Starbucks transactions are made via its popular loyalty cards, and that results in “huge amounts” of data, Mr. LaCugna said, but company isn’t sure what to do with it all yet. The same goes for social media data, he said. Starbucks has a team who analyzes social data, but, “We haven’t figured out what exactly to do with it yet,” he said. It’s a common refrain among brands, and many of the speakers and attendees here at the conference.

 (At Starbucks, Data Pours In. But What to Do With It? – Published in 2013)

In 2007 and 2008, Starbucks’ CEO Howard Schultz was forced to come out of retirement to close hundreds of stores, and rethink the company’s strategic growth plan. This time around, Starbucks took a more disciplined, data-driven approach to store openings and used mapping software to easily analyze massive amounts of data about planned store openings.

The software analyzed location-based data and demographics to determine the best place to open Starbucks stores without hurting sales at other Starbucks locations. The software is also helping to determine where the next 1,500-plus stores should be placed not only to help the company expand, but drive revenue for new store developments.

 (How Big Data Helps Chains Like Starbucks Pick Store Locations — An (Unsung) Key To Retail Success – Published in 2014)

Starbucks is an interesting case because it’s collecting all the right data to be implementing something like Macy’s is doing, but doesn’t seem particularly motivated to try it just yet. Indeed, a year ago they admitted they didn’t have a clear strategy for using the data generated by their loyalty program.

This year, they’ve put forward a clear case that benefits them in an area that is very important to the franchise; store location. For retailers, this has always been an absolutely essential part of decision-making when it comes to growing their profits and customer base year over year. It makes sense that Starbucks, having lost so much money with bad calls on store locations in the past, would choose to use their data for determining where they can gain the most long-term growth and profit when planning new locations.

This is a great example of uses for Big Data that aren’t as intuitive as customer experience related ones. It speaks to the kinds of use cases that speak to CEOs like Schultz – nothing fancy or weird, just better quality and higher quantifies of information being used to answer a question they would have been asking anyways as part of their overall strategy. It goes to show how powerful Big Data can be when you use it to approach an established goal from a new direction.

It does, however, raise a few eyebrows. Starbucks is considered an industry leader in many regards, but the reluctance to use their glut of data for more than just location planning could be a serious miscalculation by the chain. My prediction is that one of their competitors will figure it out sooner than they do and offer a data-driven, social-media-integrated loyalty program that tailors its messaging and rewards at a clearly higher level of sophistication, and when this happens Starbucks will either swiftly release an imitation or else find themselves with a significantly reduced grip on their core market.

Still, that’s not to say that it’s a bad idea to be using their data in the way they are now, and I expect other retailers will adopt the methodology in 2015 – many of them already are.

What to Expect in 2015

We found that 62 percent of retailers report that the use of information (including big data) and analytics is creating a competitive advantage for their organizations, compared with 63 percent of cross-industry respondents.

To compete in a consumer-empowered economy, it is increasingly clear that retailers must leverage their information assets to gain a comprehensive understanding of markets, customers, products, distribution locations, competitors, employees and more. In this industry deep dive, we examine industry-specific challenges, as well as provide our top-level recommendations for retail organizations.

IBM

 

The number of retailers initiating Big Data projects in 2014 jumped sharply from the number in 2013. The adoption rates are increasing steadily and the industry is soon to reach a tipping point and the arms-race will begin.

Although there has been plenty of hype around Big Data this year, the explosion of in-earnest technology implementations has yet to begin. 2015 looks like it will either be the beginning of this tipping point or will be the year where one or two huge successes will hit the news and initiate the rush to follow suit.

Core to these major successes will be the ability to source both internal and external data for these analytics. The next-best-offer that can build on data about you from your Twitter and Facebook profile, sharpening its understanding of your wants and needs, will have a clear advantage over algorithms that only use past purchase data. The loyalty program that understands what kind of incentives motivate you using the same kind of connected information across multiple platforms will similarly benefit.

We expect to see early adopters achieve great success with leveraging the in depth understanding gained from social media – to target their customers or loyalty program members as individuals. We also expect that the results (and market share gain) of those early adopters will kickstart similar projects within a larger number of retailers.

We also anticipate an increasing number of implementations of new micro-segmentation models. Retailers using classic segmentation approaches (for example, one classic model uses just 66 segments and only updates their characteristics once per year) will start considering big data enabled dynamic segmentation models with larger number of segments, where the segments are updated potentially weekly – to better reflect the dynamic nature of today competitive environment

All in all, 2015 looks like it will be a very exciting time for retailers.

Topics: allsight Big Data Big Data Analytics bigdata Customer ConnectId Data Lake for Retail Retail

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Posted by infotrellislauren on Monday, Sep 1, 2014 @ 10:16 AM

This article was featured in the Q3 2014 edition of Loyalty 360‘s Loyalty Management magazine.

Consumer Packaged Goods (CPG) companies have accepted for many decades that the reality of the industry is that the customers are interacting with intermediaries like digital merchants and retail outlets, not directly with them. The store gets to develop the relationship with the customer and the CPG company has to bridge a bigger gap, targeting end-users with broad strokes like TV commercials or billboards.

It’s hard to develop a sophisticated targeted marketing campaign or a customized loyalty offering, after all, when all of the customer data is being generated by the customer-store relationship, not the customer-product relationship. Stores typically have little incentive to offer detailed information about sales and other interactions to CPG brands – they naturally prefer consumers to be loyal to the store rather than loyal to the product brand names sold within, especially if the store offers their own branded products.

old coke sign

Ultimately, it can be tricky to make a connection when there’s a middle-man between you and your customer.

Not being able to easily connect has presented a number of challenges for the CPG industry in particular.

One of the overarching challenges is related to product development and promotion: a limited understanding of the customer can lead to imperfect offers and imperfect promotions.

That limited understanding is typically achieved through market research. CPG companies had to find alternative ways to gain insights about their target markets. Focus groups, surveys and coupon campaigns are costly and are all in some way imperfect (they provide limited data; they are based on small sample sizes; they are often not very timely etc.).

Big Data has the potential to change all of this. By analyzing millions and millions of social media comments, CPG companies are able to identify who purchases and uses their products. They can also determine the profiles of those consumers: what are their hobbies, what are their favourite TV shows, what initiatives resonate with them and are important to them?

It’s been said that social media networks are the ultimate focus group. It’s instant, uncensored customer feedback at a massive scale – and the ability to harvest this data and crunch it for analysis is providing CPG companies with a level of insight that was unimaginable just a few decades ago.

Not only is the customer feedback finally directly accessible, social media and other digital communications provide channels through which the CPG companies can speak directly to individual customers, bypassing the store entirely. This allows them to nurture relationships with end-consumers well beyond “hoping they see the billboard for the new cereal on their commute to work”. This opens the door to actual relationship-building tactics that companies in other industries have been using for years but have traditionally been unworkable for CPG.

Which leads me to the main question I want to pose:

 

 

Can investments in Big Data capabilities make direct customer loyalty or CRM programs achievable for CPG companies?

Historically, the Consumer Packaged Goods (CPG) industry didn’t see much potential in traditional loyalty or CRM programs; because the retailers selling their products were the ones interacting with the end consumer, it was hard to reach out to, establish, and then nurture relationships with individual buyers.

 

“Traditionally, CPG brands have few options when it comes to impacting purchase behavior in third-party retail environments, other than relying on costly in-store displays to grab shoppers’ attention. They also miss out on direct access to purchase data, which makes it difficult to know which marketing levers they can pull to get more of their brands into the shopping basket at checkout.” (Punchtab)

 

Both CPG executives and expert industry observers have expressed skepticism in the past that a traditional loyalty program is a good fit for CPG.

“Consider the average loyalty program pays out under 2% for every dollar you purchase,” Jason Dubroy, VP managing director, Shopper DDB says. “Someone buying a $5 box of cereal [will get] less than $0.10 [from the] loyalty program. People may eventually realize the effort for them to enter 50 pins isn’t worth the value of the program.” (Strategy Online)

 

It’s true that attempts at CPG loyalty programs in the past have proved too high-friction for consumers to really engage much with them. That isn’t the case anymore. As social media moves from being just another advertising platform to a being potential source of data and two-way customer communication, giving CPG brands the direct access to consumers that was once unattainable, industry leaders are considering the possibilities for a shift in their attitude towards loyalty.

There is an unprecedented opportunity for CPG companies to begin building deeper and more profitable relationships directly with consumers. Whereas loyalty programs were traditionally used by companies who owned the point-of-sale, today CPG marketers are able to leverage loyalty and analytics software to recognize & reward loyalty in an entirely new way.” (Crowdtwist, 2)

 

Again, social media and other sources of big data about the customer offer the opportunity to avoid relying on the retailer, which has long been regarded as one of the biggest road blocks to CPG customer analytics at the level of the individual.

The move into the loyalty space would offer up CPG [companies] more transactional data to deal with (cutting out the middle-man retailer, Dubroy adds). “Retailers traditionally can give CPG companies about as much info as a bouncer [does] about who is at the party,” Sarna says. “Loyalty is much more like being the socialite who can wander around, who knows everyone and what they’re thinking.” (Strategy Online)

 

The ability to finally isolate the individual and cater to them is an exciting and relatively new opportunity for the CPG industry.

 

“By being able to aggregate and attribute engagement, social activity and spend back to individuals, loyalty programs offer CPG marketers the ability to finally identify which of their efforts are most effective at stimulating consumer behavior and converting people along their path-to-purchase.” (Crowdtwist, 6)

CPG companies have already started experimenting with social and mobile targeted personalization of advertising and communications.

 

“One of the challenges with mobile is that it is such a personal device and impersonal messaging, whether is push message, an ad, or an offer, it doesn’t matter what it is or who it is coming from it is not well received,” John Caron, vice president of marketing for Catalina Marketing. “We know to how to leverage historical purchases, data and analytics to be able to identify and drive the right campaign in order to allow that to define the media that you see in app or mobile web on your smartphone,” he said. (Mobile Marketer)

Indeed, the key word here is “personalization” for many of the emerging use cases that combine Big Data, loyalty and the CPG industry. How are they crafting the profiles and personas that they use to customize messages and offers for customers? The answer to this lies in linking internal and transactional data with external and social data; once a company has the power to match each real-life consumer to their online identity, they unlock a wealth of data that allows them to treat those individuals with a high degree of personalization.

 

“By combining consumers’ digital and social profiles and behaviors with real purchase data, CPGs have the ability to understand which online behaviors increase awareness, trial, preference and overall buy rates, while optimizing marketing effectiveness by focusing on the channels that matter most.” (Punchtab)

What does “personalization” mean in terms of tangible steps a business can take? Bazaarvoice proposes:

 

“A brand can pre-sort reviews on product pages based on information gathered on the visitor. For example, a college student may see reviews first from other consumers identified as students. As you learn more about a shopper via purchase history, mobile app usage, online feedback, and the interest graph, tailor experiences to that individual. Use dynamic display and mobile ads to serve products the shopper is likely to enjoy alongside opinions from people with similar needs and tastes.” (Bazaarvoice, 4)

 

Using customer data for the purposes of microsegmented marketing messages is a well-documented use case, but only recently has the data needed for this level of sophistication been accessible to the CPG industry. It’s a strategy that has been proving its ROI for a few years already.

 

“In a recent study involving more than 300 CPG brands and 80 companies, Nielsen reported that “CPG brands can experience a return of almost $3 in incremental sales for every dollar spent on online advertising that has been precisely delivered using purchase-based information”. When you consider the potential impact that better sources of data can have for an industry that spends more than 25% of the global advertising budget, the implications are astounding.” (Crowdtwist, 4)

Beyond just more targeted advertising, the introduction of a loyalty or CRM program that works on the same principle of personalized offers and intelligent audience engagement has the potential to drive increased revenue beyond any one promotion or product.

 

“CPG companies can develop umbrella loyalty programs across their entire family of brands, rewarding consumers when they buy and engage with any of the brands in the portfolio. These programs can be highly effective in increasing trial, driving preference, and boosting cross-category purchase: a recent PunchTab survey showed that 73 percent of moms would be interested loyalty programs for a parent company, and 59 percent of moms would buy other products from the parent company if doing so resulted in more loyalty points — with 46 percent indicating they would even switch from a competitor’s product.” (Punchtab)

 

So what’s the potential payoff for pioneering CPG companies that launch loyalty programs using all this new customer data?

“Approximately 50% of people who enroll in a CPG loyalty program remain actively engaged with that brand on a monthly basis, and they interact with the brand 2.5x more often than the brand’s average consumer. Members who enroll in a CPG brand’s loyalty program are more likely to open branded emails, and are more likely to clickthrough on emails that contain a call-to-action. On average, our CPG client partners have experienced an increase of 109% in email open rates and a 25% increase in click-throughs for their program members.” (Crowdtwist, 3)

The opportunity is clear; Big Data technology, enabling companies to gather social data and accurately match it to transactional data, may be the missing piece that makes investing in a loyalty program feasible and advantageous for companies operating in the Consumer Packaged Goods industry.

I argue that loyalty for CPG companies has never been intuitive or a perfect fit in the past, but it is now. In some respects, the CPG industry has the advantage of starting late – as they have no legacy systems to deal with, they have a clean slate. Companies investing in loyalty now will be doing it with 20+ years of established best practices in one pocket and the incredible technology available today in the other.

Perhaps even more importantly, companies can come at this with fresh eyes and a willingness to think above and beyond how things “have always been done”, because so few CPG companies have ever done much in the loyalty or CRM space before. Creativity, cleverness, and the ability to make full use of the tools and information available is a potent combination.

CPG companies are in an interesting position to potentially go from rarely bothering with loyalty to completely revolutionizing what can be accomplished with a loyalty or CRM program in a very short span of time.

Whether or not they will is another question entirely.

InfoTrellis Inc. is the creator of Customer ConnectId™, the Big Data solution that provides a deep understanding of customers using patented identity resolution and data matching technologies. Customer ConnectId™ is enabling CPG companies to gain an understanding of their customers in ways which were not possible before for applications like micro segmentation, CRM or loyalty program initiatives.

 

Topics: allsight Big Data Consumer Packaged Goods CPG Customer ConnectId loyalty

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Posted by infotrellislauren on Wednesday, Jun 4, 2014 @ 9:25 AM

Wouldn’t it be cool to pay for parking with points accumulated at your favorite local coffee shop? What about getting a new pair of shoes with points earned by frequently picking up bread at the local bakery? Or going out for a round of golf courtesy of the dry cleaners you visit all the time?

Small businesses can’t compete with big chains or corporations when it comes to loyalty programs. They don’t have the capital to invest in building massive, innovative programs, and often have to depend on something simple like a small stamp card, which doesn’t provide them with any customer data or differentiate between higher or lower value customers.

Impressive loyalty analytics, customized rewards, and easily tracked points are often the mark of a large company – these sorts of features usually aren’t accessible to small businesses with less data and fewer resources.

A company in the Netherlands called Shopper Concepts is looking to change that.

 

Image5

 

In partnership with InfoTrellis, Shopper Concepts is creating a loyalty management and analytics program for the connected, savvy customer. This loyalty platform, called Buzzoek, is emerging as a unique and powerful tool for small business owners. Throughout Amsterdam, local businesses that use this loyalty platform pool their resources, sharing transactional data for more powerful analytics, connecting to better learn the individuals who buy from them and what their likes are. They can even enable customers to use rewards points earned at one store in any other store that uses the same program.

Consumers can even share their rewards with friends (or even total strangers!) via Facebook, Twitter or Email. The team behind the Buzzoek loyalty program has also partnered with UNICEF to allow program members to donate their rewards directly to charity.

 

Image1

 

They even combine their rewards programs into one convenient tap card across all of the involved businesses – Buzzoek converts a card the customer already has in their wallet (like a transit pass or a library card) into a rewards card that can be used at any one of the fifty locations using their program.

With an established foothold in the city of Amsterdam, the company is building a networked web of small local businesses that have the loyalty data and analytics capabilities of a massive corporation.

This isn’t the first example of people using communal resource pooling to sidestep the involvement of corporations. With consumers coming together to crowdfund projects and products they want to see, local communities launching their own internet using rooftop wifi antennas, and even groups of programmers creating goofy cryptocurrencies that they then used to send teams of athletes to the Olympics, people are increasingly coming together to accomplish previously unachievable things in unison with other invested individuals.

Shoppers Concepts is already in full force with its dozens of connected stores, bringing together businesses like Alexander Hairstylers, Chocolate Company, and Doppio Espresso to reach exciting goals with their loyalty programs they would have struggled to accomplish in isolation. Using the InfoTrellis AllSight Big Data Insights Engine, Shoppers Concepts is making bold new steps towards simplifying things for consumers and enabling small businesses to work together to serve them better.

 

Image4

 

Suddenly, small businesses can interact with and reward their customers like the big businesses do. Considering the fact that small businesses are typically more flexible and connected with their community than larger ones, could this signal the beginning of a new era of small entrepreneurs fueled by next gen loyalty technology?

Topics: allsight Big Data buzzoek Customer Loyalty loyalty partnership shopper concepts

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Posted by infotrellislauren on Friday, May 2, 2014 @ 4:03 PM

When you’re in the hotel industry, even the absolute most loyal of loyalty members will stray to the competition from time to time. Figuring out how to identify and combat this tendency is increasingly falling to technology, which has made dramatic leaps and bounds in data discovery when it comes to increasing loyalty member walletshare.

 

One technology that is driving progress for hotels in this arena is the Big Data powered “Customer ConnectId™”, a solution built by InfoTrellis to gather and match online social profiles to loyalty member profiles.

 

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Like any new technology, the proof is in the pudding – and that pudding is the first customer use case. With this particular identity resolution technology, the first use case is taking place within the prestigious offices of one of North America’s leading hotel chains.

 

In the hands of the hotel’s analytics team, the ability to connect loyalty member profiles up with their public online activity (like Twitter accounts, Facebook accounts and travel website reviews) became a powerful tool in their search for better strategies for increasing customer loyalty and, ultimately, loyalty member walletshare.

Initial work with the massive new sources of data, automatically matched and consolidated into customer records, provided fascinating information about how people indicate travel when they interact with social media. The hotel’s analytics team discovered:

  • Comparing customer check-in rates to those same customers’ FourSquare and Yelp check-ins with competing hotels, they were ahead of their competition
  • BUT, comparing customer check-in rates to those customers’ FourSquare and Yelp check-ins with their own hotels, they only broadcasted about 50% of actual check-ins, suggesting that:
  • The average socially active hotel guest only checks in to FourSquare or Yelp about half of the time

 

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This meant that while at first glance they seemed to be retaining their loyalty members with a high degree of success, more rigorous scrutiny revealed that they were likely staying with the competition as much as 50 – 60% of the time when they travelled.

 

To provide a higher rate of accuracy when it came to pinpointing when their loyalty members were staying with the competition, the analytics team expanded their search to include check-ins at airports and restaurants far from the person’s hometown – both good indicators of travel. They also started leveraging the technology’s Natural Language Processing capability to incorporate informal, unstructured “check-in” messages, like a casual mention or reference to travel in a tweet or an update.

 

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The end result of all this hard work is that now they have a much clearer idea of which of their loyalty members are staying with the competition and how often and, eventually, will be able to build models that help them to understand why they chose to stay with the completion. The immediate benefit is that their loyalty and marketing teams now have an unambiguous target list of loyalty members who travel frequently without staying with them – an obvious opportunity for the most growth with the least effort, if they can target them with the right offers and messages to win back a greater share of their business.

 

The next steps for their test of the technology will be to build out the depth of the profiles by pulling extra dimensions from the social data that can be used to understand their motives, behaviors, and preferences.

 

By employing a machine learning algorithm they expect to be able to get as much information as they can about their 10+ million loyalty members and then, using this complex pattern identification technology, accurately predict things like hobbies, interests, life events, job title and family status for those loyalty members who don’t list them explicitly. From there, ten million loyalty members suddenly become much easier to understand and custom-tailor offerings and experiences for.

Topics: allsight Big Data Customer ConnectId hospitality social data social media

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Posted by infotrellislauren on Wednesday, Jan 29, 2014 @ 11:29 AM

This is an abridged 4-page summary of the full 25-page whitepaper, which can be found in totality at http://infotrellis.wpengine.com/insight.php

 

Why Should I Change My Loyalty Program?

 

Hotel industry loyalty programs are failing to promote true loyalty.

 

Airlines and hotel chains – widely regarded as the masters of the loyalty program – are faring no better than the rest of the business world in terms of actual customer loyalty. According to our survey of 4,000 travelers, hotel loyalty program members are not loyal to their preferred brand and loyalty programs drive undesirable brand-switching behavior.” (Deloitte)

Despite the industry-wide investment in rewards programs, the impact on sales numbers has room for improvement. “Travelers spend as much as 50 percent of their spend with non-preferred brands and 65 percent  of high frequency travelers report having stayed with two or more brands in the past six months.” (Deloitte)

Hotel loyalty programs aren’t delivering ideal results. “The best-case scenario is that hotel loyalty programs as they are constituted today have either little or no impact on travelers’ purchase decisions, and, worst case, these programs drive undesirable brand-switching behavior.” (Deloitte)

There is a glut of identical loyalty programs with no meaningful differentiation.

 

Hotel Loyalty Program Memberships in 2012 reached an approximate total of 223,550,000. (COLLOQUY) Nothing is stopping those customers from subscribing to every hotel loyalty program that appears before them and, indeed, many do. “Our research found that approximately 45 percent of hotel travelers, and 80 percent of high frequency hotel travelers, hold two or more loyalty cards. Of the high frequency travelers, 41.6 percent are members of four or more loyalty programs.” (Deloitte)

In particular, the notion of collecting points towards a reward is no longer unique or especially motivating. “Accumulating reward points towards a free night’s stay was meaningful at one time—before the landscape became saturated with loyalty programs and consumers’ kitchen counters were littered with account numbers and point-summary statements.” (Deloitte)

 

 

Customers are expressing a desire for better experiences, not better prices.

 

In the US hotel industry in just 2013, 6% of hotel customers switched preferred hotels due to an inferior customer experience. (Accenture) That 6% represents billions of dollars of lost revenue.

50% of customers who switched could have been retained just by being made to feel more appreciated. (Accenture) Customers who would forsake a better price for a better experience are not a gentle-hearted minority. 31.7% of mobile savvy customers, many of a generation considered by traditional wisdom to be fickle and price-motivated, surveyed by Aimia fell into the category of “Experience-Seekers”, who “value the best experience, not just the price.” More customers fell into this category than any other category. (Aimia)

This is no small trend – all referenced studies made a positive link between better customer experience and higher brand loyalty. “Past customer experience trumps loyalty programs. High frequency travelers rated past experience as being the most important attribute to their overall hotel experience.” (Deloitte)

 

Billions of dollars in unclaimed loyalty and wallet share are waiting to be captured.

 

“Genuine loyalty drives share of wallet, migrates customer behavior, and, ultimately, enhances shareholder value.” (Deloitte) There is undeniable potential for revenue increase for those companies who can secure the loyalty of their competitor’s customers.

24,590,500 hotel loyalty program subscribers are highly likely to switch and do not feel compelled by today’s loyalty program models. The total unaffiliated and at-risk walletshare from hotel loyalty program members without strong attachments to any one brand is approximately $20 billion per year. (See appendix). This means there is $20 billion of annual hotel spend up in the air that has not been “claimed” by loyalty to any one hotel chain.

 

 

How Should I Change My Loyalty Program?

 

Differentiate from your competition by personalizing and customizing interactions.

 

If a hotel wants their loyalty program to be memorable and unique, this represents an opportunity to get ahead of the competition. “Even in industries such as hotels only 36% of customers acknowledge receiving a tailored experience.” (Accenture)

“To build affinity and loyal customers, hotels should consider reinventing what their customers overwhelmingly consider to be uninspired loyalty programs that lack personal and customized experiences.” (Deloitte)

It is the superior customer experience that will help to secure the loyalty of the younger generation, too. “Empowered by technology and influenced by social media, [members of the new generation of travelers] make informed travel decisions and are likely to give their attention to hotels with personalized, differentiated loyalty programs.” (Deloitte)

 

Leverage a deep understanding of the customer by adopting a more data-driven approach.

 

Traditional wisdom or common assumptions about customer segmentation is not effective in understanding the modern customer. “Many businesses fail to utilize valuable consumer data collected at enrollment and point of purchase to differentiate their loyalty programs across customer segments.” (Deloitte)

Organizations are increasingly turning to data mining to drive better business decisions and customer experiences. “More and more companies are seeing the value of offering loyalty programs and – more importantly – the value of tracking, reporting, and drawing actionable insights from customer data.” (COLLOQUY) This data is absolutely essential for providing each customer with the ideal experience at every point of contact with the company.

“Mining [customer] data will likely produce a rich understanding of discrete customer segments with distinct service preferences. These data-driven insights can be used to determine which customers’ brand loyalty is critical to build and maintain.” (Deloitte)

The loyalty program revolution will happen; the early adopters will profit the most.

 

“We expect the entire loyalty industry to grow, on average, in the years to come. But those companies that study the data […] will be the ones to finish first in terms of growth, and will make the most of the economic comeback.” (COLLOQUY) This next step in loyalty program evolution, says the research, is all but inevitable. Those companies that hesitate and lag behind will likely find themselves losing their “at-risk” loyalty program members to more data-driven programs that deliver a highly customized and ultimately more impressive customer experience.

 

For the full article, which includes the full appendix, please download the unabridged version of this whitepaper. http://infotrellis.wpengine.com/insight.php

 

Bibliography

Deloitte: A Restoration in Hotel Loyalty: Developing a blueprint for reinventing loyalty programs

Deloitte: Rising above the Clouds: Charting a course for renewed airline consumer loyalty

Bulking Up: The 2013 COLLOQUY Loyalty Census

Accenture 2013 Global Consumer Pulse Survey

Aimia: Showrooming and the Rise of the Mobile-Assisted Shopper

Topics: Big Data Customer ConnectId Customer Loyalty hospitality hotel industry loyalty loyalty programs Retail social media whitepaper

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Posted by infotrellislauren on Monday, Dec 2, 2013 @ 1:14 PM

A little while ago, Software Advice — a BI technology reviews site — put out a piece on their blog titled “David Norton’s 4 Secrets to Understanding Customers Through Analytics”. It’s a good read, if you haven’t seen it, and it absolutely verifies the work that we’ve been doing at InfoTrellis to provide clients with the ability to act on the advice that thought leaders like David Norton have been hammering home over the last couple years.

 

Right from the beginning of the article, it describes an analytic approach to customer loyalty and marketing that more and more organizations are moving towards.

 

“Every move made by customers at Caesars’ properties is tracked, the data feeding into Caesars’ central customer database–data that is later mined to improve the casino’s direct marketing efforts.”

 

Ten years ago this might have sounded like a fantastic ideal, but today it’s becoming more common and a rapidly increasing number of companies in a wide range of industries are taking advantage of the benefits of this strategy. Data mining isn’t just for scientists and hip young startups, and proper use of social media data puts money in the pockets of companies unafraid to make the transition.

 

Norton breaks his strategy down into four key notions, which he defines as:

 

  • Create simple customer segments first by mining data;
  • Prioritize data collection projects to build momentum;
  • Abandon intuition. Test offers to optimize marketing campaigns; and
  • Ensure marketing analytics is embedded with decision makers.

 

It’s a good read, if you’ve got a few minutes.

 

It all sounds great, but how do you get started on the path of making this into something tangible? The actual process of mining the data, cleaning the data, and then matching it to internal customer records is a daunting project. Consider getting experts involved to help with the heavy lifting; the InfoTrellis suite of Big Data Solutions offers a range of products from social media monitoring to social data collection and matching with internal customer records.

Topics: allsight Big Data blogging Customer ConnectId Social Cue

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Posted by infotrellislauren on Thursday, Nov 21, 2013 @ 8:50 AM
Topics: Big Data canadian retailers infographic Retail Social Cue social media

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Posted by infotrellislauren on Thursday, Oct 24, 2013 @ 9:12 AM

 

With the show wrapped up and the recollections of many exciting conversations settling from a busy flutter into a determined simmer in the back of my mind, I thought I would sit down to try to write up my top ten takeaways from the DMA Show.

 

1. Everybody wants social CRM or social CIM in one form or another.

From the companies on exhibition to the narratives of the session presenters, the conversation kept coming back to the question of consolidating social data and other digital data (website clicks and the like) with the more traditional types of information about the customers. This is where everyone is going, and whether they’re gliding towards it full speed or staring at it over budgetary barriers with longing but unhopeful eyes, ultimately this seems to be an inevitable progress towards much more centralized and integrated data collection. I won’t be surprised if five years down the road social CRM is the industry standard with companies who have not adopted it slowly fading away.

 

2. Hardly anybody was using the phrase “big data”.

From a conference billed as “data-driven marketing” I expected to hear a lot more of this popular term, especially with Nate Silver as one of the keynote speakers. Instead it was a mix of “big data”, “social data”, “interaction data” and “unstructured data” – which I think I’m happy about, because it’s moving towards more self-explanatory naming conventions, even if they’re much narrower in scope than “big data”. I’m still waiting to see if someone coins a better way to refer to it.

Ultimately, marketers aren’t going around asking for “big data” – they’re asking about more concrete things like social, interaction, clickstream, next-best-offer. These are all things that are powered by “big data”, of course, but the allure and the message isn’t the data itself, it’s the end result.

 

3. Social Media monitoring without any context is now outdated.

Now that the industry is a few years down the path of collecting information from social platforms, we’re seeing a clear divide between the first generation tools that cropped up to meet that initial, experimental wave of adoption and the second generation tools that are being asked for now by marketing professionals. Social Media data without any context is now considered very thoroughly old-school – that is to say, nobody is impressed by a tool that can’t tell you more about the people talking. Where are they? Who are they? What do they mean to me? Are they existing customers? Loyalty program subscribers?

The greater the disconnect between the comments being harvested and the real customers they represent, the less value organizations see from collecting those comments. Companies are asking “why settle for a half-baked technology that only goes so far?” If you’re going to start using social media for customer service, for example, why would you use a social media response technology that isn’t at all integrated with your email and call center response technology? Information is meaningless without context.

 

4. Marketers don’t just want to know what to say to customers – they’re starting to think about when to say it.

It was very obvious that the industry is evolving and evolving fast. One of the signs of this steady maturation is that conversations around social media marketing have gotten beyond the basic questions of “which channel” and “what to say” and started to get more detailed and granular. How do I know if this is the perfect opportunity to convert a competitor’s customer to my loyal customer? How can I tell if I’m about to lose a customer unless I respond immediately? When is the perfect time to send which message, and over which channel? Do I time my campaigns to be a simultaneous blast, or is it more effective to stagger them over a longer period?

The more details we start asking for, the more scientific the answers must become – and the more complex and sophisticated the marketing industry becomes. It’s really interesting to see the best practices that are emerging and developing in this time of dramatic change.

 

5. The better the tools get, the more there is to take into consideration.

What I mean by this is that as we see more amazing capabilities for collecting the data, especially digital data, the question becomes what to prioritize, how to consolidate it, and what to use it all for. Even immersed in the industry as I am, walking around the show floor made me realize what a glut of digital touchpoints marketers have to work with. Customer Interaction Management technology has exploded into a thousand different super cool and super specific directions, from mouseover heat maps to in-store cellphone based location tracking on where people stop in a store and how long they stop there. Google Glass type technology is even promising the ability to see what parts of a website catch someone’s attention and what parts get ignored by the consumer’s ever-flitting eyes. There is an immense number of ways to measure how people interact with a brand online and dozens of different channels, each with their own unique types of interactions.

As cool as this stuff is, what will be interesting going forward is seeing what gains traction the fastest and which technologies get folded together. How much touchpoint monitoring is micromanagement and how much is ignoring – even wasting – valuable information?  With how sharply focused some of these technologies are, I expect to see them consolidated not by the end users or the original architects but rather by bigger tech companies who see the potential of a valuable synergy and buy up these super specialized companies to add sophistication or new capabilities to their existing technologies. This is the strategy we’re seeing employed by companies like Google, SalesForce, IBM, etc. – acquisitions that lead to more bundled, wide-net technology offerings than anything a specialized startup has the ability to offer.

 

6. Marketing agencies are hungry for second generation social monitoring.

You might imagine that the early adopters of new technology will be the big businesses with the big budgets. I was actually quite interested to find that the most eager inquiries at the show about how we take our social data and match and consolidate it with internal customer data were actually coming from marketing agencies who work with these sorts of companies.

It’s possible this is linked to the demographics of the show, but I also think another possible reason for this is that marketing agencies are so sharply focused on marketing technology and so savvy with the many tools out there that they’re keen to give themselves an edge by knowing as much as they possibly can and uncovering the new tools with the greatest potential so that they can be the ones to introduce them to their clients.

 

7. Nobody is doing much in the way of actually matching the data between social understanding and traditional understanding of the customer.

In talking to these eager marketers and walking around the tradeshow floor with a pocket full of technical questions, we found out that nobody is doing the sort of matching we do, if they’re doing any matching at all. In fact, we found that a lot of organizations attacked the problem of integrating your social and digital touchpoint data with your internal customer data from the completely opposite direction. They started with a really cool UI that did lots of neat things, like identify the moment when a customer was at the peak of purchase intent or automate customer service responses that provided a range of reactions on social media depending on customer importance, and then worked backwards to put together something that would enable it.

The result is that a lot of people aren’t doing any matching, treating a Facebook account and a Twitter account and a LinkedIn account as three unique people even when they all belong to the same individual and having no idea which customer they are, if any. Some people are doing highly simplistic matching that is dependent on one or two pieces of information being the same, like an email address or a home phone numbers, and without that piece of information they’re helpless to make the connection.

(Our really cool matching technology doesn’t depend on a linking data point like an email or a name – if you want to know more about how we do it please feel free to send me an email and I’ll happily chat your ear off about it.)

 

8. There seems to be surprisingly little fear of or conversation around issues of data quality.

Maybe it’s because data quality just isn’t a very sexy topic, but I realized after the show that not a single person had expressed any sort of concern around rushing in to this “big data” stuff, trusting technology, and then ending up with a database full to the brim with garbage and bad analytics and faulty conclusions.

I know I would be a bit afraid of that, and to be honest I was expecting it to be something that the slow-to-believe crowd would bring up as one of their reasons to hesitate. Instead it seems like that’s less on their mind than the danger of getting zero value out of investing in big data technologies. Maybe the thought of worse than zero value – active harm – is just too much to fathom. I just hope data quality governance isn’t a question that organizations are forgetting entirely.

 

9. Business cases for big data are increasingly – almost overwhelmingly – abundant.

Another stumbling block for people wondering about this risk of “zero value” is that the use cases are all over the map. On the one hand, it’s amazing how many ways you could improve an organization using social media data alone. On the other hand, if you rattle off a laundry list of ways the technology could help, it can become overwhelming – not just for the end user, but for the technology vendors themselves. We met some organizations that had technology similar to ours but had focused on a very cool but completely different business case in how they focused their product development, and it shaped us into very different solutions when compared side by side.

So here’s the question: do we want to show organizations a shallow glance at thirty different business cases for big data, or is it better to focus in on two or three in great detail? Is it riskier to specialize, gambling that there will be enough demand within the niche you’ve decided to cater to, or is it riskier to be a generalist without one compelling message?

There’s also the question for the marketing executives looking to invest in new technologies, too – are they looking for a magical solution that has an impact on every part of their business, or should they prioritize and go for something specific to one particular business goal or pain point?

 

10. Using big data technology effectively is a challenge of goal-setting and imagination.

“What does the typical big data and social CRM client look like?” I got asked this particular question a few times, and the more I think about it the more convinced I’ve become that the two unifying traits of companies that do big data and do it well are the ones that have:

  1. A very specific result or business goal in mind before they start work, and
  2. The creativity to imagine how big data technology can accomplish this result

Big data is amazing because it has heaps of potential – but that’s exactly what makes it tricky. The last thing you want to do is fall into the ‘technology for the sake of technology’ hype. The teams that have accomplished incredibly impressive business results using big data are not the ones with the most money or the biggest projects; they’re the ones asking questions like “what if we could…?” and “can I apply this to…?” and “imagine if we tried…?”

Start with a business problem, and then apply a little theoretical whimsy – it might surprise you how much today’s technology is capable of. Success is just a matter of how cleverly we apply that technology.

Topics: Big Data Big Data Quality Industry Marketing social media Twitter

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Posted by infotrellislauren on Tuesday, Jul 16, 2013 @ 11:40 AM

Everybody, it seems, is getting onto the social media bandwagon. You can’t get far into any discussion about information management or marketing without it coming up, and it’s fascinating to see the emerging best practices and strategies behind social media products and consulting groups.

Here are five lessons from over a decade of working with Master Data Management, a much older piece of data-wrangling technology, that will serve any marketing or IT professional well as they navigate the social media technology landscape.

 

1. Huge Investments are a Tough Sell

I’m going to assume if you’re reading this that you see value in social media marketing, or else you see the potential for value. If you’re looking to leverage social media for your organization at a scale and level of sophistication higher than a summer intern firing off tweets now and then under the corporate handle, you’re going to have to actually spend money – and in an organization, that can be easier said than done.

Master Data Management teaches a very simple lesson on the subject of talking to your executives about a wonderful, intangible solution that will surely provide ROI if they can find it in themselves to approve the needed budget. The lesson is this: the bigger the price tag, the harder time you’ll have convincing a major decision maker it’s a necessary or worthwhile investment.

Often with MDM the more it’ll cost to implement, the more fantastic of an impact it will have on the data within the business. With social media, that’s a little harder to prove. It doesn’t help that there are more “social media marketing solutions” out there than you can shake a stick (or a corporate credit card) at.

If your executive doesn’t have time for your technobabble pitch for a million dollar overhaul, try wiggling your foot into the door by starting small without a lot of commitments. For MDM, that’s a proof-of-concept, and there’s no reason that can’t be applied to social media marketing. Consider starting off with something that is subscription based (my more IT-minded colleagues would refer to this as “software as a service” or “SaaS”) to give your management the confidence that if they aren’t seeing returns, they can just turn off the subscription and stop spending money on it.

 

A high level dashboard application is an ideal place to start.

 

This is your social media marketing proof-of-concept – if your initial test run gets you great results, that’s a good sign that your organization is part of an industry that stands to really benefit from a bigger, more expensive social media based project. Maybe even something that involves the term “big data”, but let’s not run before we walk.

 

2. Consolidated Records Mean More Accurate Information

This is the core premise of Master Data Management as an information management principle: you want there to be one copy of an important record that consolidates information from all its sources in the organization, containing only the most up to date and accurate data. It’s a simple but powerful idea, the philosophy of combining multiple copies of the same thing so that you only have one trustworthy copy, and then actively preventing new duplicates from cropping up.

The same thing applies to social media, especially when we’re talking about the users as actual human beings and not as individual accounts across multiple channels. Face it, we’re not interested in social media as an abstract concept – we’re there for the people using it.

(Which is why I love to cite this actual exchange between an older gentleman of a CEO and his marketing manager that goes something like: “I don’t get Twitter. I don’t use it, I don’t want to use it, I don’t personally know anybody that does use it, and I think it’s stupid.” “I agree. I honestly think it’s stupid too – but that doesn’t change the fact that 90% of our customer base uses it, and that’s why we need to pay attention to it.”)

So we’re there for the people – why on earth would we approach gathering and visualizing metrics and data on user accounts instead of people? Should we treat the Facebook, Pinterest, Twitter, LinkedIn and Tumblr account of one individual as having the weight of five individual voices?

What you really want to be looking for is a solution that matches and combines users across multiple channels. This isn’t quite the same process that it would be as part of MDM – this is new ground here that needs to be broken, and if you want to figure out that a Facebook user is the same person as a Twitter user, you need to be a little more creative than just checking to see if they have the same name.

With access to less traditional data (like a phone number or an address) it takes a bit of new technology combined with new approaches to match social media accounts accurately. I won’t bother getting into the details here, but suffice to say it’s something that today’s technology has the ability to do and a couple of companies are actually offering it. It seems perfectly logical to me that if you’re going to seriously use social media, especially in any sort of decision making process, you need to have a consolidated view of each user instead of a mishmash of unattributed accounts, which would, without a doubt, skew your numbers one way or another.

I’m going to briefly mention that if you want to take it a step above and beyond for even more insight into your customers, you can further consolidate that data by matching it to your internal records – Joe B in your client database is Joe B on Facebook and JoeTweet on Twitter, for example – but this is a much more ambitious project.

 

3. Data Quality is Not Just An IT Concern

Master Data Management is intended to bring greater value to an organization’s data by making it more accurate and trustworthy. Whether or not that actually happens very strongly depends on the quality of the data to begin with. As they say, “garbage in, garbage out,” and that’s even more true of social media marketing solutions. If you thought the quality of data in your organization was sorry to behold, I have a startling fact for you: the internet is full of garbage data. Absolutely overflowing with it. Not just things that are incorrect, but also things that are irrelevant.

If you’re going to get facts from social media, you’d better start taking data quality seriously – and make sure whatever solution you use is built by someone who takes it even more seriously. Let me give you an example.

Suppose you’re a retailer who sells Gucci products. You have a simple social media solution, a nice little application that gives you sentiment analysis and aggregate scores. You investigate how your different brands are doing and, to your shock, find that Gucci has a horrible sentiment rating. People are talking about the brand and boy are they unhappy.

You do some quick mental math and determine that it must be related to the promotion you just did around a new Gucci product. The customers must hate the product, or the promotion itself. You hurriedly show your CEO and she tells you to pull the ads.

What you didn’t know, and what your keyword based social media monitoring application didn’t know, is that there is a rap artist who goes by Gucci Mane whose fans tweet quite prolifically with reference to his name and an astonishing bouquet of language that the sentiment analysis algorithms determined to be highly negative.

Your customers are, in fact, pretty happy with Gucci and the most recent promotion, but the relevant data was drowned out and wildly skewed by a simple factor like a recording artist with a name in common. This wasn’t a question of “the data was wrong” – the data was accurate, it was just irrelevant, and the ability to distinguish between the two requires technology built on a foundation of data quality governance.

If you’re going to use social media data, especially when you’re using it as a measure for the success of a marketing campaign and subsequently the allocation of marketing budget, make sure you’re paying attention to data quality. Don’t veer away in alarm or boredom from terms like data governance just because they aren’t as sexy as SEO or content marketing or 360 view of the customer – train yourself to actively seek the references to data quality as part of the decision making process around a social media strategy.

 

4. Don’t Let Someone Else Define Your Business Rules

One of the most time consuming aspects of preparing for a Master Data Management implementation is sitting down to define your business rules. There is no one definition of the customer and no one definition of a product. These are complex issues that depend heavily on the unique needs and goals of an organization, and don’t let anybody try to tell you otherwise.

To that end, social media marketing demands the same level of complexity. If you’re building a social media strategy, you absolutely need to be thinking about those business rules and definitions. How do you define a suspect? A prospect? A customer? What makes someone important and worth targeting to you? Is it more important to you to have fifty potential leads or five leads that are defined by very specific requirements for qualification?

Every organization will be different, and a good social media solution takes that into account. Be wary of a piece of software or a consulting company that has a set of pre-established business rules that aren’t easily customizable or – even worse – are completely set in stone. If an outside company tries to tell you what your company’s priorities are and applies that same strategy to every single one of their clients, thank them for their time and look elsewhere.

Also steer clear of a solution that oversimplifies things. If you’re looking to social media opinion leaders as high value targets, you want to know how they’re defining that person as an opinion leader. Are they using one metric, like Klout score or number of followers? Are they using five? Would they be willing to give more emphasis to one over the other if your company places more value on, say, number of retweets than on number of likes?

Good solutions come preconfigured at a logical setting that is based on best practices and past client success – but are also flexible and able to match themselves to your unique business definitions and strategy as much as possible.

 

5. Data Silos Are Lost Opportunities

Finally, I want to talk about data silos. I’m going to expand on this term for those of you reading this who are marketing people like me and not necessarily information management junkies (although I confess the people who are both combined in one are always a delight to talk to). A data silo generally refers to situations in which the different lines of business hoard their databases and don’t like to share their information throughout the entire organization. This can be a huge problem for Master Data Management adoption, because of course the point is to make it so that everyone is using the same data, but it’s also a problem for social media marketing.

Social media data, first of all, is not just marketing data. Your sales teams will undoubtedly have uses for it in terms of account handling, and your product development teams, if you have them, will be interested in learning more about what customers actively crave from the market, and heck, your customer service division almost certainly can make use of an application that instantaneously warns them when people are dissatisfied.

The fact is, if you want to prove that gathering this data is useful, don’t hoard it all to yourself. Share that data around and let people play with it. Creativity – and creative ways to use data – happens when people think about things in ways they don’t normally think about them. Traditionally social media has been relegated to marketing, but it doesn’t have to be.

An ideal social media solution, even one of those affordable subscription-based ones I’ve been talking about, presents the data in an accessible, easily shared format. The good ones come with both a high level dashboard in business terms that even a CEO who thinks Twitter is stupid can log into and gain insight from and also the ability to drill down and export raw data so that the people who want to do complex and unique number crunching have that ability without the restraints of the program itself.

 

Shown above: Social Cue™, the InfoTrellis social media solution

 

It’s important to have a good balance of goal-oriented strategy – never go into social media without a plan or a purpose – and openness to innovation. It’s even more important to be working with an application that accommodates both.

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InfoTrellis is a premier consulting company in the MDM and Big Data space that is actively involved in the information management community and constantly striving to improve the value of CRM and Big Data to their customers. To learn more about Social Cue™, our social media SaaS offering, contact the InfoTrellis team directly at demo@infotrellis.com to schedule a product demonstration.

Topics: allsight Big Data data governance Data Quality Marketing Master Data Management mdm Social Cue social media Social Media Marketing

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