Everyone Shops for a Different Reason

By Jeanne Roué-Taylor

Customers are rarely a homogenous group. They differ greatly in revenue potential, levels of loyalty, and frequency of contact. What makes loyalty work, therefore, is an ability to segment your customers using powerful analytics. Segmentation allows the right amount of effort and right offers to be applied in the right moments to the right customers.

Knowing enough to segment properly takes powerful analytics. Unless analytics are a key part of your loyalty platform, odds are you won’t be able to do much more than old-fashioned transactional loyalty.

But segmentation isn’t such a simple thing. There are common segments based on how people shop—like price or convenience—but below those broad categories, there are more subtle ways to segment based on what is required to engage a customer in the right moment with the right offer. This is the description of behavioral segmentation, and takes a capability to test and learn that wasn’t available until recent times.

Test and Learn

Test and learn provides a way to find segments that may not otherwise be visible to marketers. By choosing existing segments and testing offers on a smaller scale, new segments can be developed and marketed to in a larger way. The opportunity to test and learn is a benefit of a customer loyalty platform that includes powerful visual analytics.

In the end, everyone shops for a different reason. Marketers armed with the tools to not just segment along traditional lines of age, race, and other factors—but also to test and learn—have a distinct advantage over those who don’t.


Customer Engagement Requires Knowing Your Crowd

By Jeanne Roué Taylor

How well do you know your customers? While most brands would say they know their ideal shopper, how many can break down their customer engagement into finely tuned segments based on analytics? Some can, but too many are still behind the curve when it comes to having the loyalty platform to gather data, visualize and discover patterns of people and behaviors, and respond with the right engagement at the right time. Having a high level of customer intimacy enables right-time marketing and it’s within reach of most businesses (learn about right-time marketing in this whitepaper). We’ve entered a data-rich age where there are fewer and fewer excuses for not knowing your customer crowd.

How to Know Your Crowd

For starters, every customer has unique attributes that we refer to as their characteristics. That set of information includes permanent things like gender and age, but also includes things like past history of interaction and purchases that paint a clearer picture than simple demographics. While age, gender, affluence, and other factors give us broad stroke indicators of buying propensity, finely tuned segmentation requires a richer set of information than traditional groupings.

The second aspect of knowing your crowd is tightly coupled to how your customers communicate with you, which we refer to as channel. These are the pathways of interaction preferred by your audience, and getting the channel right is the difference between having a seamless conversation and being intrusive. Brands that can’t listen and respond across the many, sometimes simultaneous, channels of today’s marketing can’t say they truly know their customer.

The third aspect of knowing your customer is having a context for each moment of interaction. Knowing whether a customer is currently in the act of shopping, at the time of purchase, or just in the information-gathering phase has a strong effect on what interactions are most appropriate and what the timing should be for engagement.

Characteristics, channel and context together are what define truly knowing your customer crowd. Without them, your ability to increase intimacy, gain loyalty, and increase total lifetime value are negatively impacted.

Watch the webinar on right time marketing and learn how to use characteristics, channel and context to determine the best time to market.

The 12 Days of Christmas – Loyalty Lab Style

by Jeanne Roué-Taylor

With Black Friday behind us and Christmas just weeks away, retail establishments across the world are in their busiest time of the year. Are you maximizing the surge in holiday business? With that in mind, here’s our carol for you:

12 Days of Christmas, (Loyalty Lab style)

On the first day of Christmas, my system gave to me:
My customer’s undying loyalty.

On the second day of Christmas, my system gave to me:
2 mobile apps,
and my customer’s undying loyalty.

OK, OK, you get the point. Let’s just skip to the final tally:

On the twelfth day of Christmas, my system gave to me:
12 million tweets a tweeting
11 market segments
10 systems talking
9 clever campaigns
8 real-time offers
7 million web hits
6 retired systems
5 ways to sell
4 million page likes
3 months of forecast
2 mobile apps
and my customer’s undying loyalty.

So even if you don’t get everything you want in your stocking, we at least hope your customers turn into fans ­­– and you become a marketing hero in 2013!
Happy Holidays from the Loyalty Lab team…

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The Power of Analytics to Drive Loyalty – Part 2: Predictive Analytics

Last week, we gave you an overview of David Rosen’s webinar focusing on The Power of Analytics to Drive Loyalty. This week, we would like to dive a little deeper into one of the most interesting and pressing topics he discussed: segmentation models that marketers have the opportunity to apply when managing and analyzing their loyalty campaigns. In the Webinar, Rosen breaks down segmentation into three approaches and highlights their benefits:

1.   Profitability Segmentation

  • Prioritizes and manages members in order to increase profits and retain customers
  • Focuses on a bottom-up value calculation model that takes into account actions and accruals
  • Drives program activity, targeting APIs

2.     Needs and Behavioral Segmentation

  • Understands members and customizes how we communicate personal, relevant offers
  • Offers multi-variable cluster analysis with manageable sense of segmentation factors and actionable segments that will form outward-facing personality of the program

3.     Predictive Analytics Segmentations

  • Provides day-to-day analytics approach for offers and continuous learning
  • Allows for multi-variable regression for using past performance and ongoing testing to best predict the outcome of ongoing offers

Rosen particularly focuses on the power of Predictive Analytics. This approach can give marketers a new kind of insight into their customers and allows them to analyze any campaign down to its ROI. By comparing a group of consumers, stores, or products subject to a campaign versus a control group that did not participate in the campaign, marketers are able to achieve the kind of in-depth analysis they desire, and make it a relatively seamless experience as well. So in addition to learning about the behavior of individual customers, marketers can get a true sense of the effectiveness and productivity of certain campaigns.

TIBCO’s predictive analytics starts with a propensity model, which looks at a number of variables that reflect a customer’s past purchases and future shopping baskets. Looking at these factors allows marketers to ask, and answer, the most pressing questions of any and all loyalty programs: how do we match the right offer, the right incentive, the right motivator, to the right consumers at the right time and place? How can we maximize the effectiveness of that incentive, and the overall productivity of the campaign?

These analytics allow you to tailor your campaigns down to the specific consumer. You now have the ability to understand why one consumer is more likely to buy one item, while another is more likely to buy something completely different, while a third may not be likely to buy anything at all, unless a 20% discount was offered.

What does this mean for you, the loyalty marketer? Unprecedented insight into your customers’ behavior, the effectiveness of your tactics, and a clear-cut path of next steps to grow your ROI.

In our next post, we will focus on ‘Offers Looking for People, and People Looking for Offers.’ 

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The Power of Analytics to Drive Loyalty – Part 1: Desires vs. Capabilities in Analytics

TIBCO’s David Rosen recently presented at the Loyalty 360 Engagement Expo on a topic that we at Loyalty Lab find to be undeniably essential in today’s loyalty marketing landscape: the importance of analytics, and the ways in which more comprehensive reporting can grow the success of your loyalty program.

Marketing Analytics

We surveyed over 100 marketers to assess what analytics tools they are currently using; the types of metrics, or dashboards they have; and the capabilities they’re investing in to take their loyalty programs to the next level.

Ultimately, we found that there is a huge gap between the desire to be able to do more complex modeling — from a day-to-day analytics approach to the ability to analyze the success of a specific campaign on a per-customer basis — and the internal capabilities of various marketing organizations.

The survey was broken into three segments. In the first segment, in which we measured the success of loyalty programs, the biggest gap was seen in measuring specific campaigns using rigorous test and control. This refers to the ability to analyze any campaign down to the individual ROI by comparing a group that was subject to the campaign versus a control group of consumers, stores or products that did not participate in the campaign.

The other major gap in measuring success was using tools to measure social buzz and sentiment. This area is obviously ripe for investment over the next few years. Currently, about 40% of marketers are using test and learn from a scientific standpoint to analyze loyalty programs, but 80% of marketers think that everyone should be doing so.

The second segment of the survey measured the gap between the importance and efficacy of dashboards and reporting. Here, the most noticeable gap was in segment migration, or the ability to move profitable customers to higher tiers, as well as retain customers already in high tiers.

The final part of the survey measured the interest in and need for specific data analysis and analytics capabilities. Rosen cites a number of advanced analytics that can be beneficial, but one approach stood out to all of the marketers surveyed as an enormous boon for a loyalty programs: the ability to match members to specific offers, and to optimize these individual offers. As Rosen put it, “Marketers dream to be able to achieve this kind of relevance for the customer.”

In our next post, we will review the three types of segmentation models Rosen suggests deploying to optimize your loyalty program. Hear more from him and see “The Power of Analytics to Drive Loyalty” webinar in its entirety here

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