Analytical Engine

Segmentation initiatives often fail, not because of their design, but because of their execution. In many cases, management is so dazzled by the promise of an elegant design that they neglect to focus on the hard work of actually solving a business problem. Our recent work with a global pharmaceutical shows how to avoid that problem by deploying a simple yet powerful “build/plan/execute/monitor” model. The success of the execution depends upon key metrics around targeting performance (time spent per customer), sales performance (average order size) and resource productivity (effort to conversion).

The global healthcare company relied on a specialized sales force to sell a particular high-end drug directly to consumers. Success depended on the sales force building strong customer relationships and customizing product offerings to each customer’s specific needs.

Step 1: Build a ready-to-use targeting tool
In this case our segmentation approach relied on classifying customers based on two key metrics: by1) customer potential, which reflects a customer’s overall appetite to consume the client’s product, and 2) by customer’s share of potential which indicates percentage of potential captured in terms of revenue dollars.
However, independent of the segmentation approach one follows, the key factor is to translate the approach to a ready-to-use targeting tool which gives sales force reps the flexibility to view the customer classification. (In this case, the customer classification could be viewed on a 2X2 matrix by various filters such as ”geography” and ”last order month.”)

Targeting Tool Overview

Step 2: Plan for the roll out
A solid pre-execution plan can ensure that there are no surprises during the execution phase. We created an exhaustive checklist of key tactical planning steps. Significantly, we also assigned ownership and timelines for each activity. Some of the key areas to focus on as one thinks about a successful execution are:

Step 3: Execute the roll-out
Execution generally tends to be the most arduous phase and requires close tracking and monitoring. In this case, ensuring a means of capturing regular feedback from the sales reps as they got into the targeting and sales process was a critical component of the execution phase. There are two key elements here:

Step 4: Analyze results
A deployment story is half baked if management is not able to assess the performance of the solution over time. Building the right measurement plan is a start, but real value only comes when the data is analyzed in ways that deliver insights that will inform management decisions.
A critical part of the feedback loop, apart from what is discussed in Step 3, also falls in the analysis phase: incorporating the learnings from the metrics into the segmentation algorithm. The metrics and numbers give the real picture of the sales force performance. Beyond that, it also speaks about the efficacy of the target list. Then, the idea is to make refinements to the segmentation algorithm based on the learnings from the pilot process.

Execution Results

In this engagement, our build/plan/execute/monitor approach, along with the buy-in and required compliance from the sponsor of the program,ensured a successful roll out of the solution. Our segmentation solution identified “high maintenance” customers who took approximately two hours of a rep’s time during a particular sales visit, but also generated enough business (up to five products per order) to justify the extra attention . We also helped the sales force reduce the time and effort they spent with some low-potential customers and refocused efforts on the tele-sales channel which cost less and required only about 20 minutes of phone conversation to convert customers. That initiative freed up account managers to focus instead on high potential, low share-of-wallet customers.

Ian Ayres, in his book Super Crunchers, questions decisions made through intuition and advocates decisions made by optimal utilization of data. In the book, Ayres gives examples of how data driven decision making is impacting businesses, education, sports, government, etc. One such industry where a lot of data is collected but not necessarily used in decision making is the gaming industry. Despite Harrah’s being an analytics poster child, not a lot of gaming companies leverage analytics to its potential.

Casinos collect revenue and customer data from a variety of sources, such as player cards, slot machines, and gaming tables. They can also gather information from non-gaming sources: through call centers, surveys, hotels, restaurants, and events. Harrah’s is a pioneer in collecting detailed data on its customers’ activities. Harrah’s team sliced that data into finer segments ”identifying unique customer groups and targeting each group with pitch-perfect marketing strategies.” The results -  tremendous increase in customer loyalty that has turned Harrah’s from a relatively unremarkable player into an industry giant.

Recently, we worked on an engagement for one of the large gaming companies in the world, where the casino wasn’t able to capitalize the rich data it had on its players and slot machines. Direct marketing campaigns and slot machine floor layout optimization, were two big initiatives that were performed but without leveraging the rich information in the data. An analytics driven approach was needed to identify high potential players and optimize slot machine layout. In this post, we will talk about the approach we used to segment players and identify changing behavior patterns of players.

Though a lot of advanced concepts of customer analytics can be applied here, we used a basic RFM (Recency, Frequency, and Monetary) segmentation to effectively target players. RFM was tweaked to LFM (Latency, Frequency and Monetary) for a simple reason- unlike a website or a grocery store, where a person’s recent visit shows that he/she is likely to visit again in near future, a casino trip happens after some interval. So it makes more sense to calculate whether days between the consecutive trips are changing significantly. In an LFM cube the cutoff of each cell of cube can be determined using basic business rules based on frequency distributions.

One of the KPI that was tracked was average spend per trip, and due to this, some players with higher number of trips and medium spend are undervalued and under-targeted. The LFM cube gives us the visibility into such customers and helps us evaluate the true potential of such players. Such a cube can be run at regular intervals to capture the movement of players between the cells, and such movement is a good predictor of changing behaviors and can be an effective lever for targeting marketing campaigns.

In next post we shall talk about how analytics can be used around slot machines and how such inputs can be used to design the floor layout in a better manner.

In our earlier post, we shared our thoughts on how data-driven personas can add the much needed value to attitudinal segmentation.The power of “personas” can be leveraged not just in traditional consumer marketing, but across other functional areas such as “franchisees”, “agencies”, “sales force”, etc.

We recently noted a similar parallel in the recruitment space.

There is little doubt on how recruiting continues to receive a keen attention from human resources function of every organization. ˜Who makes a good hire? “and ˜Who is best armed to succeed at the firm?” are questions that recruiters are constantly trying to answer. Recruitment is vital not only because it helps the organization meet its objective of talent acquisition but also because recruitment decisions impact the overall firm performance.

Given this context, we wanted to understand how an employee’s pre- joining profile (educational qualification, prior work experience, source of hiring, joining level) impacts her performance at the firm. For this, we applied rigorous data analytics techniques (decision tree algorithm) to

• Classify the employee pool into various performance based segments - high, average and low performers.
• Identify the variables that best discriminate one segment of employees from the other.

Based on our segmentation approach, we helped the recruiting team by creating personas to represent different employee segments. (For more thoughts on segmentation, click here). In this case, a persona is a profile sketch of the ˜desired hire” from the perspective of required skill sets, behavioral and attitudinal characteristics. Segmentation and creation of personas proved to be a vital exercise for the following reasons:

• The first step to effective recruiting is to fully understand the type of employees the firm wants to hire in terms of required skill sets, behavioral and cultural fit; personas help different stakeholders in the process including recruiters, interviewers, search firms, recruiting agents, share consistent understanding on the desired hire.
• Personas give a well rounded description of the employee by covering attitudinal and behavioral characteristics; this knowledge can be effectively leveraged by recruiters in answering questions like ˜how do we reach the potential candidate?”, ˜what kind of media should we use to advertise job postings?” and so on. This results in fast and speedy sourcing of candidates.
• Most importantly, personas are data driven and an outcome of rigorous analytics. To that extent, it simply shows what has worked and what has not worked for the firm in the past.

As the authors in ˜The Risky Business of Hiring Stars” rightly point out, hitching your wagon to the rising stars is not the solution to a recruiter’s woes. It is imperative for recruiters to devise specific tailor made recruitment strategies for different roles and profiles. Employee segmentation and persona creation is a key step in this direction.

Last but not the least, the prerequisite for the success of this analysis is the data itself. Firms need to start or continue investing in the right kind of performance management softwares that continuously track and maintain employee performance metrics.

George Olsen talks about the importance of personas as a valuable design tool in driving strategic and tactical level details.

Personas are fictitious characters that are created to represent different product user types and to aid tactical and strategic level decisioning. Typically, ad agencies have relied on qualitative methods such as interviews, focused group discussions, existing literature to develop a typical character sketch. However, a well designed persona is one which not only suffices an ad agency’s need but also addresses the interests of other stakeholders including marketing, sales, finance and HR teams.

Recently, our client, a financial services firm, wanted to understand the attitude and behavior of their users. They wanted to strengthen their brand architecture and develop the right kind of messaging for target audience. We expanded on our market segmentation approach and used the granular primary research data, company’s transaction data and third party segmentation schema (Claritas) data to do develop personas in a bottoms-up fashion. The data driven approach of building personas leveraged internal data, survey data, and a third party segmentation schema (Claritas P$YCLE).
We followed a four step approach to develop the personas:

1. Conducted a survey to capture the attitude and behavior of respondents towards financial issues and planning
2. Used a statistical technique called principal component analysis to reduce some 40 attitudinal and behavioral variables captured from the survey to two dimensions - expectation from the service provider and level of financial anxiety. We used cluster analysis to classify the respondent pool on these dimensions.
3. Built personas by mapping the clusters to the Claritas segments so as to capture information around media preferences and lifestyle behaviors. We enriched the personas with survey information including demographic indicators, behavior towards financial service providers, financial anxiety, as well as Claritas segments that helped highlight media, lifestyle, and income-producing asset ownership traits
4. Performed a “day in life” analysis to understand how a typical day for a persona would look like in his or her diary. This analysis explored the various lifestyle and media touch points which can be used for tactical marketing initiatives

Our approach to leveraging rapid market segmentation helped our client not only to understand the segments on the basis of attitudinal dimensions but also to appreciate the strategic and tactical initiatives that can be achieved with robust data driven personas.  This level of understanding of their customers helped them improve their segment specific marketing messaging and customizing the store experience for each segment.

As the US and the world economies encounter a downturn and firms look to scale back, Marketing is often one of the first places to face budget cuts.  Forrester reports that many companies expect to cut their marketing budgets by 3%.  But how do you maintain or grow your customer base and revenues when consumers are spending less and your message isn’t getting into the marketplace as loudly?

We think the use of online coupons deserves a harder look.  Emailing your customers and prospects with newsletters, product updates, and coupons is certainly nothing new, but it’s now well-positioned for even greater success:

• Companies are getting good at it. After dabbling in techniques like SEM and direct email, firms have gotten better at driving profitable growth from these methods, and many are increasing their focus on online advertising as a cheaper way to spend their marketing dollars. 
• Consumers want more of it. During these uncertain times, consumers plan to increase their use of coupons to save some money.  Sending these options straight to their inbox or mobile phone accomplishes that goal and positions you as a preferred provider.
• Consumers who use it are attractive prospects. Compared with consumers who only use offline coupons, Forrester reports that users of online coupon tend to have higher incomes, shop online, like to try new products, and influence peers.  Younger consumers also use coupons, and they can be a good avenue to get the word out about your product.
• More data is available to help you win at it. More firms sell marketing lists (or can help you run campaigns to get new lists), segmentation data helps you understand consumers’ preferences and desires, and syndicated data helps you understand purchase behavior.  Combining this data gives you incredible insight into consumers to tailor unique marketing messages.

You don’t just want to throw promotion dollars at existing customers to give them discounts on things they were already going to buy; rather, you likely want to use those dollars to deliver positive returns and achieve business goals - such as acquiring new customers, increasing market share, or increasing wallet share.  Doing this requires targeting offers to customers based on their stage of the customer life cycle:

1. Acquire. Coupons can be a good tool to help consumers overcome the risk associated with trying a new product; if a new product is cheaper than the one they normally use, the savings might be worth trying.  You can use them to attract entirely new customers to your firm, or to get your existing customers to try a new product line.  Targeting early adopters can also help generate buzz, as they will influence friends and family to buy the product as well.
2. Grow/Stimulate. Once you’ve acquired a customer, you want them to maintain or increase their purchases.  Two ways of stimulating usage are encouraging them to try a different variety (e.g., color, size, flavor) or showing them new uses for the same product (e.g., using Q-tips for craft projects in addition to hygiene).  In this stage, the focus should be on the marketing message, the coupon being used to help seal the deal and drive the customer to the store.
3. Manage. In this stage, your customers are steady-state users, and couponing may not be required to retain them. However, these consumers present a good opportunity to test new offers on an already loyal customer base and measure the response before using them on the general public.  You might test them using different demographics, layout, or wording, perhaps even running controlled experiments to determine which of two offers is more effective.  We’ve done some research on the use of Behavioral Economics to improve offer design, which might be helpful in performing this testing.
4. Reclaim. If customers reduce their consumption or begin to try competitors’ products, you can use targeted offers to reintroduce your product and retain them as customers.  However, depending on their needs and your product pipeline, you may otherwise opt to move back to the beginning of the life cycle and acquire them as customers of another of your products.

This strategy requires a high level of customer insight to understand preferences and stages in the life cycle.  You can gain this insight by applying segmentation schemes to your lists of customers and prospects, and by analyzing your customers’ history of purchases and coupon redemption.  Applying a rigorous testing approach will help you identify the most effective offers for each customer and stage.

Applying this framework to understanding your customers and targeting coupons will deliver several benefits, including:

1. Strong ROI potential.  Campaigns that are more effective and lower-cost, targeted at attractive customers, have a stronger potential to deliver a positive ROI.
2. Better data to analyze results. Results of online campaigns are easier to track and measure than traditional campaigns, particularly if your coupons lead customers to purchase from your own website.  Analyzing results from campaigns that involve multiple partners may require a different approach, as Vishal outlined in his earlier post on trade promotions.
3. Better customer relationships. You can use the insight you’ve gained about your customers’ behaviors, preferences, and purchase history to continually develop targeted offers.  This level of personalization will help you deliver the right offers to the right customers at the right time, and ensure that your promotion dollars are spent most effectively.

A recent article in the New York Times discusses the impact of price on the perceived effectiveness of drugs.  The article describes an experiment where two groups of patients receive a placebo drug that they are told is a pain reliever, but the groups are told different price points.  After taking the placebo and receiving electric shocks, more people (85%) who were told it cost $2.50 reported pain relief than those who were told it cost only $0.10 (61%).  While the placebo effect is well-documented, this experiment highlights an important application to business – that a product’s price can be an effective marketing lever that can directly impact its effectiveness and value.

This is not the first time that cues such as pricing and packaging have been applied to marketing.  We recently collaborated with Professor Ariely (also quoted in the article) to explore the impact of applying behavioral economics principles to online marketing strategies.  One of the concepts we discussed in the resulting paper, is “Framing,” and we explored how consumers evaluate their options on relative terms, and make purchase decisions based on the cues given to them.  In the paper, we highlight the example of a magazine publisher who was able to steer consumers toward a higher-cost option simply by presenting a lower-cost one.  Even though no one chose the lower-cost option, it was an effective cue in that it showed the relative value of the higher-cost one.  In the drug experiment, price was the cue, and the higher price led consumers to find it a more effective (and valuable) product.

In our market segmentation work, we have found that different consumer segments require different value propositions, and that marketing messages need to emphasize factors such as features or pricing to appeal to their target markets.  Many generic, store-brand products are actually the same as the name-brand products, but are packaged differently and sold at a lower price to appeal to a different consumer segment.  The drug experiment highlights the same concept – and 61% of the patients who thought it was a cheap drug still reported that the product was effective in relieving their pain.

In developing marketing strategies, it is important to carefully consider the market segments you are targeting, along with the value drivers for each.  Understanding these drivers allows you to apply behavioral economics principles to maximize ROI through optimal pricing and product framing for each segment.

A set of whitepapers for the executive who wants to leverage the data to make smart marketing investments:

1. Identifying attractive markets and understanding your customers
2. Increasing online sales
3. Focusing on profitable distribution partners
4. Improve customer experience through call center data
5. Retaining your customers

We were working for a large Fortune 500 company to assess the performance of its sales team’s incentive program. A two tier incentive structure was in place for the sales force.
·        The incentive was directly proportional to the revenue generated by the salesperson
·        The salesperson generating revenue beyond a certain cutoff was given extra perks and monetary rewards
Prima facie this incentive structure made sense but when we analyzed it carefully, an array of systemic problems was observed. We mention few of the issues with potential ideas to tackle them
1)      Setting and maintaining goals – The incentive revenue cutoffs were predefined, static and the senior stakeholders had the power to override them. The cutoffs and tier allocations were not reviewed on a regular basis, making the tiers passive for the candidates who are performing well over a period of time. 
The key is to better understand the performance drivers of the sales force by  defining and measuring appropriate metrics  (using correlations, principle components, and factor analysis). Appropriately segmenting the sales force, defining the segment cutoffs using historical data and then reviewing them on a periodic basis is critical to achieve a better return from an incentive program.
 

2)      Quality Vs. Quantity of Money - Sales force were rewarded based on the volume (revenue) of the business and not the quality of the business (margins). Targeting the wrong prospects by the sales representatives sometimes led to value negative customers.
Using lifetime value calculation (LTV) of customers instead of absolute number of customers or amount of revenue, as the performance metric will solve this problem. Most organizations do understand the theory but do not use the metric, because calculating customer lifetime value is tricky and the data is not always available to do so accurately. In our opinion, it is better to have an incomplete understanding of the lifetime value (using averages and proxies for certain aggregated costs) rather than not making an attempt to calculate and use the metric. A flawed metric is better than a wrong metric.
 

3)      Poor and inadequate data - Data to track performance and understand the drivers of success for sales representatives was not readily available. The data was collected was mostly in standalone excel sheets. They were manually reported and coded making it difficult to link the independent tables.
A culture of measurement fosters investment in better data capturing systems. As analytics is used to calibrate the incentive structures on periodic basis, investment in centralized systems to standardize the data collection process will reduce analysis cycle time and increase accuracy of the analysis.

An integrated view of the incentive program helps in incentivizing the right people for the right reasons with the right benefits. Analytics is part of the answer. However, as emphasized by most of our projects, we believe that a sustainable solution is a combination of a robust understanding of business processes, technology infrastructure and analytical techniques.
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A recent article in WSJ talks about how offline segmentation schemes and data is being used for online targeting of advertisements. The sidebar of the article mentions how Acxiom executes on this strategy

1. Acxiom has accumulated a database of about 133 million households and divided it into 70 demographic and lifestyle clusters based on information available from public sources.

2. A person gives one of Acxiom’s Web partners his address by buying something, filling out a survey or completing a contest form on one of the sites.

3. In an eyeblink, Acxiom checks the address against its database and places a “cookie,” or small piece of tracking software, embedded with a code for that person’s demographic and behavioral cluster on his computer hard drive.

4. When the person visits an Acxiom partner site in the future, Acxiom can use that code to determine which ads to show.

5. Through another cookie, Acxiom tracks what consumers do on partner Web sites.

At a client engagement we also have utilized a similar strategy (however without using any personally identifiable information like email address) to understand how segments of customers consume content on a website (see image below). This information is used to develop and personalize content on the website and can also inform site layout and design.

What we are witnessing is that companies are trying to bridge the worlds of marketing and web analytics. As we have previously mentioned, third party segmentation schemes which are so pervasive in the marketing world will increasingly become the translation layer between the two worlds.

Jim Novo, who authors an insightful marketing blog, rightly identifies that PRIZM clusters (or other geo-demographic segmentation systems) are not as predictive as a behavioral data for desired ‘action’ in the online world.

e-commerce folks are usually looking for behavior from customers, and the fact demographics are not generally predictive of behavior by themselves

Segmentation and targeting are different and PRIZM is fundamentally not a targeting tool. Even in the world of direct mail where rich behavioral data from website is not available; it is rarely used as the sole targeting criterion.

However, geo-demographic segmentation systems like PRIZM, have some benefits for which web analysts should consider using them. Here are four reasons for which we have used PRIZM in an online setting:

1. Customer data is available
When customers login to your website and it is possible to link them to their PRIZM code, you have one more variable to put into your targeting models and an additional dimension of information on your customer.

2. Tie back to non-online world
In most organizations, web analytics is another marketing silo, and senior executives do not yet understand how to relate their online and offline customers/visitors with their media buying strategy, direct mail strategy etc.. A segmentation scheme like PRIZM plays the role of a standard translation engine for the various marketing organizations to describe a customer. Identifying your online visitors using PRIZM helps your marketing department create a holistic view of the customer.

3. Hypotheses to identify content gaps
If you are a content-based site, then having PRIZM segment of your visitors associated with its rich detail on media consumption and lifestyle interests, will help develop hypotheses on the existing content gaps on the website. You will need to augment it with good market research and do rigorous testing to identify what really works.

4. Monetizing the site
Advertisers understand PRIZM – so if you have advertising inventory, then you should consider segmenting the visitors based on it.

PS: PRIZM is one of the major “geo-demographic segmentation” schemes available in the market and we have used the two terms interchangeably in this post