Tag Archive for 'Customer Profiling'

In the press: Marketing Via Invoices and Billing Statements

Published on June 30, 2010 in Leslie Nogue. 0 Comments Tags: , , , , .

Leslie Nogue, Pitney Bowes Business Insight

The July edition of Marketing AdVents features a byline authored by Pitney Bowes Business Insight’s Director of Strategy, Suresh Nair.  In the article, Suresh discusses the manner in which companies are beginning to use statements and invoices as a marketing vehicle.  By combining transactional information with targeted communications in transactional documents such as statements, invoices, and notifications, organizations are able to generate sales, reduce churn, and build customer loyalty.

Suresh argues that marketers need to own the bill, encouraging them to influce the content, format and customization of the white space typically found on statements and transactional documents.  He also outlines just how effective transactional document marketing can be, given the 95% open rate on these types of documents.  Read the full story…

Retail 2.0: Brick vs. Click

Published on April 1, 2010 in Eric Steckling. 0 Comments Tags: , , , , , , , , , .

Eric Steckling, Pitney Bowes Business Insight

CompUSA is attempting to neutralize the Internet’s one big advantage: Information. In an effort to improve the retail shopping experience, CompUSA has upgraded 23 of its 32 stores to the “Retail 2.0” version. Chief executive of parent Systemax Technology Products Group Gilbert Fiorentino has designed new product displays that include touch screen computers which provide specifications, pictures, customer reviews, competitor’s prices and other information about the products on display. The idea was to change the passive approach to retail and engage the customer. CompUSA has found that information empowers the customer and allows them to make more informed decisions, which Fiorentio says is translating to loyalty and increased traffic.

So what’s next for “Retail 2.0”? Amazon.com often gets me to buy items by simply suggesting them to me based on items I have in the cart, have already bought or even looked at. These (usually on target) suggestions are generated from a vast database of the browsing and purchasing records of myself and the other 76 million unique visitors last month.   The difference is data. Online retailers have an easier time collecting and storing customer data. Most brick and mortar retailers have been slow to collect or utilize transactional data, which is instrumental in improving stores’ customer experience.

It is evident that technology will play a key roll in helping retailers “engage” their customers. RFID controlled inventory and smart shopping carts could perform the same suggestive functions by identifying items in your cart and making suggestions based on prior customers’ cartloads. To fully leverage transactional information retailers must also know who you are, another task Internet retailers have no trouble with. Once the customer is identified, Retail 2.0 should include social media interaction as well as purchase driven targeted emails. Only time will tell what other innovations brick and mortar retailers will create to slow the market share loss to online vendors.

In the Press: Schoolcraft College Taking Strategic Approach to Targeting Potential Students

Published on March 29, 2010 in Leslie Nogue. 0 Comments Tags: , , , , , , , .

Leslie Nogue, Pitney Bowes Business Insight

In the Spring 2010 edition of Community College Technology Update, Marty Heador of Schoolcraft College discusses the value of integrating consumer segmentation data and analyses into direct mail programs.   Schoolcraft College, a continuing education college in Livonia, MI, reached out to the predictive analytic consultants at Pitney Bowes Business Insight for help in maximizing the value of their direct mail campaigns. 

Through effective customer profiling, PBBI consultants were able to identify the affect that distance has on Schoolcraft’s student response rates.  They also determined the student count was dropping and the course load was decreasing per student faster than the number of students enrolled.  By uncovering the most “in-profile” students and the “out-of-profile” students, Schoolcraft’s marketing department is now able to re-direct their direct mail towards the carrier routes within the 10 miles of the college with the highest concentrations of potential students.  This effort has allowed Schoolcraft to “gain a deeper understanding” of their target student, but has also helped focus their marketing funds on those areas with the highest ROI. Read full article…

Spotlight on the Customer: Schoolcraft College

Published on March 17, 2010 in Leslie Nogue. 0 Comments Tags: , , , , , , , .

Leslie Nogue, Pitney Bowes Business Insight

The Challenge
Schoolcraft College, a comprehensive, open door, community-based College in Livonia, Michigan was seeking to target their mass mailings to neighborhoods with the highest concentrations of potential students.

The Solution
Through in-depth analysis of customer, geographic, and demographic factors, analysts at Pitney Bowes Business Insight were able to weed out under-producing carrier routes, allowing Schoolcraft College to focus their energies on the neighborhoods most likely to produce students. As a result of the segmentation analysis, Schoolcraft College gained a deeper understanding of its target student, enabling them to communicate with with prospects more effectively.  Using Pitney Bowes Business Insight’s comprehensive customer segmentation analysis, Schoolcraft College was able to identify concentrations of in-profile students, ultimately boosting enrollment and reclaiming valuable marketing dollars through targeted mailings.

To find out how Schoolcraft was able to quantify and utlimately increase their ROI, download the case study.

Cross-selling equals customer retention…but who’s buying?

Published on November 20, 2009 in Brian Diepold. 0 Comments Tags: , , , , .

Brian Diepold, Pitney Bowes Business Insight

It’s pretty well recognized that the deeper the relationship with the customer, the more valuable it is for the bank and the customer.  For years we have been preaching cross-sell as the way to build relationships, gain lasting customers, and run a profitable bank.  It’s also generally regarded that cross-sell leads to customer retention.  There may have been a bit of chicken and the egg with this part, but I think that has been resolved by looking at the timing of most cross-selling.  As we all know very well by now, the great majority of cross-sell takes place within the first 90 days.

I’m willing to take that as evidence of causality – cross-sell does in fact lead to customer retention, not the other way around.  The luxury of having millions of customer data records is that one cannot find themselves bored.  It seemed like a worthy endeavor to quantify the actual impact of cross-sell on customer retention.  And with that, enter 80MM customer account records.  By quantifying this relationship, we can see the direct impact of a firm’s strategic initiative and diagnose the types of households where cross-sell is more important to strengthening the relationship and increasing the probability of retaining the household.  The analysis uses a logistic model to determine the impact of cross-sell on the probability of retaining a household.

The bottom line is that regardless of how you measure cross-sell ratios, you can find the intuitive relationship that more products per household lead to a higher probability of retaining that household.  We also find a diminishing marginal return – i.e., the first cross-sell is the most important to establishing the relationship.  While each additional product does increase the likelihood of retention, the biggest jump is with the first cross-sell.  Rather than rewrite the research paper, I’ll just highlight some of the key findings:

  • Moving from one product to two for the household yields the greatest increase in the probability of retention.
  • Households with low deposit dollars yield an 8 – 10% increase in the likelihood of retention through cross-sell.
  • Households with mortgages or interest-bearing deposits as the lead product also yield an 8 – 10% increase in the likelihood of retention through cross-sell.

If you have the luxury of focusing your resources on certain types of households, these are the ones where you can make the biggest impact.

Consumer Demographic Profiling: Does Distance-weighting Make a Difference?

Published on November 20, 2009 in Nat Evans. 0 Comments Tags: , , , .

Nat Evans, Pitney Bowes Business Insight

It is a standard market research practice to use psychographic segmentation as a primary tool for discerning a company’s target customer.  This “customer profile” creation is a primary means by which customer behavior is bucketed into distinct groups that reflect differing customer characteristics, shopping behavior and loyalty to a retail, restaurant, or consumer package brand.

Over time, the Strategy & Analytics statistical modeling team at PBBI has given a lot of thought to the idea that customer profiles, and their use in sales forecast models, may be enhanced by weighting the customer source survey data by distance.  It makes sense.  The farther away target (or non-) customers are, the more pronounced the profile scores may be.

For instance, a typical customer profile for a high-end department store, with a specific high income, suburban customer segment or “cluster” (Corporate Clout, say, from Acxiom’s PersonicX lifestage segmentation system) may have a score of 200, meaning that people within the segment spend two times what an average customer spends for the concept.  A low-income segment (Single City Stress, for example) may have an index score of 40, or the segment spends 40 cents for every dollar that an average customer spends. Weighting the profiles by distance, however, may yield a more intensified result.  You may expect the high-income cluster to go from an index score of 200 for customers only within 0 to 3 miles, to 225 for those same customers beyond 6 miles from any given store.  Perhaps the Single City Stress cluster would go from 40 to 25, meaning that the farther away the cluster is from the store, the less they are willing to patronize and spend at the department store relative to other customer segments at the same distance.

In theory, it seems to be a reasonably insightful approach.  In practice, the S&A modeling team created just such an analysis, and found that among several clients’ customer databases, distance has no significant bearing on the relative spend of psychographic segments at the same distance.  The following box plot will give a sense for one sample profile’s distribution:

Distance Weighted
As shown, 50% of all scores’ distributions fall between approximately 50 and 110.  A couple of outlying clusters find themselves floating outside the distributions (the “1”, “2”, or “3” above the whisker for each plot), but statistically, no significant difference was found between scores at different distance increments.  The distributions’ medians were roughly the same; variance was the same.  This pattern was consistent among several customer files we tested, and the application of several distance weighting methods yielded no statistically significant enhancement whatsoever.  Goes against the hypothesis.  To be sure, there exists a multitude of ways to carve up customer data, and this analysis is by no means definitive, but from a macro level, it seems the proof is in the data. This analysis also does not mean that distance in its myriad forms (straight-line, drive time, drive distance) has no influence.  Obviously, it does.  Distance decay portrayed on a sales per capita, or other relevant, basis is very important indeed, and is an extremely well documented predictor of consumer behavior.  It’s just a matter of proper application, and what any one retailer’s customer data is truly telling an analytics researcher.

Are There Limits to “Profiling” Your Customer ?

Published on September 16, 2009 in Gary Faitler. 0 Comments Tags: , , , , , , , , .

Gary Faitler, Pitney Bowes Business Insight

In the arena of retail site selection research, we have been a vigorous proponent of the need for customer profiling via segmentation. Our approach has emphasized the bucketing of customers into distinct segments, reflective of differing levels of customer engagement and spend. This segmentation, based on variable demographic, behavioral and psychographic characteristics, ferrets out your most productive customers (i.e. the 10% that generate 40% of sales) and equally important, identifies your least productive. Understanding who these customers are, and where they are concentrated geographically is one of the pillars supporting an effective retail business, as it contributes to critical decisions ranging from store site location (or retrenchment) to the prioritization of investment in marketing dollars.

Pitney Bowes Business Insight has also pioneered the concept of “effective population” in which the segmentation profile becomes the means of adjusting actual population to create a foundational construct, influenced by shopping behavior. This allows us, in one quantitative statistic, to wed the variables of mass and profile. The approach has been extremely useful in rationalizing distance decay patterns across divergent segments.

We have noted recently, however, that a number of our clients are pursuing a “silver bullet” in their approach to customer profiling. We suspect that, as a result of the difficult times confronting retail generally, decision makers have turned to new market research professionals, hoping to ensure the best approach for optimizing sales in a climate of “down” markets. For some of our clients, these professionals, who tend not to think spatially, may, at times, be fostering an inflated view of customer profile – possibly to the exclusion of other real estate fundamentals. As noted above, we view segmentation as one of the pillars supporting effective retail execution and site selection, but others are equally critical.

As champions of the concept of segmentation and customer profiling, we must also emphasize its appropriate use. The importance of customer profiling is variable based on the level of differentiation of the customer. For example, a high-end purveyor of fitness and athletic equipment would represent an extremely segmented profile, while a ubiquitous quick service restaurant would represent an equally extreme non-segmented profile. Both concepts, in the end, would be highly dependant on what we call the fundamentals of sound real estate site selection: convenience to critical population mass, retail synergy and visibility/market exposure. However, for the highly segmented concept, any application would additionally be extremely sensitized to store positioning vis-à-vis the most in-profile customer base.

For some of our retail and restaurant clients that have mass appeal (i.e. minimal differentiation within the customer base), we have recently observed exaggerated expectations from the customer profile. They appear fixated on deriving some insight into their customer base that will drive a deeper penetration than realized to date. As much as we’d like to glean from their customer data some new profile-driven formula for uncovering optimal store placement, in these instances, it is primarily the real estate fundamentals, on which we have traditionally focused, that remain the best predictors of sales potential.

It may be helpful for marketers to think of the brick and mortar unit as a “touch point” itself, with physical convenience and outstanding execution the unique features driving customer response.

[More insights on site selection research from our Predictive Analytic consultants].