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.

Trade promotions represent a significant part of the cost of consumer packaged goods (CPG), and an important part of a go-to-market strategy, it is the second highest expenditure after COGS and represents two thirds of marketing spend. Yet, there is little visibility into where this spending actually goes, or how effectively it increases revenues, expands market share, or creates brand awareness among consumers. The irony of the system stands affirmed with the fact that extra pressure generated over the supply chain for immediate deliveries is borne by the end consumers which has pushed the actual monetary benefits reaching the end consumers down to 50% over the years.

Trade promotions have taken various forms and attained different levels of complexities as the markets and available technology have matured over time:

1. Slotting allowance: reward paid to a retailer for the retailer’s shelf space
2. Case allowances: reward offered to a retailer when merchandise is purchased by the case; greater the number of cases, greater the discount
3. Account specific promotions: reward for specific big/small accounts (retailers)
4. MDF or market development fund: a lump-sum payment made once or twice per year which the retailer may (or may not) spend on marketing the product
5. Scan back promotions: reward for higher sales, where the retailers are required to submit paperwork as a proof of the sales, to qualify for the trade promotion allowance

Data at the most granular level is required for effective management of trade promotions, but most of the time it is not available or is highly fragmented and inconsistent (at times the most granular level data might be available in segregated laptops !). Combined with the differences in geography and final pricing at the retailer level, firms are most of times unable to break down trade promotions in sub-processes. Given the level of complexities involved, the market essentially lacks standard meaningful metrics to rate a promotion at an absolute or even at a relative level within the firm (*more than 70% of the trade promotions are not evaluated properly and less than 30 % are considered profitable). Eventually, many firms end up taking the outcomes of the promotions rather than the whole process as a proxy for the performance metrics, thereby making continuous improvement a challenge if not impossible.

One way we have approached the analysis of Trade Promotions is performing the following steps:

1. KPI Identification: Identifying the key parameters involved within the system will crucially determine the effectiveness of the whole procedure. Some common metrics include:
• Time periods i.e. the average active period of a promotion and the time difference that may be involved to measure its effect.
• The expected increase in the sales volume.
• Consumer growth.
• Discounts reaching the customer or customer pass-through.
• Increase in the shelf space.
• Increase in revenue/profit/profit margin for every dollar spent in trade promotion
• Increase in the no. of cases sold per dollar spent in trade promotion
• Increase in brand value over the specific demographics
2. Data Gathering and Quality Audits: Gathering the data from sources that may include invoice history, third party/syndicated data (e.g. IRI/Nielsen), retailer level sales data or POS data, and even individual laptops.
3. Data Preparation: Involving processes like missing value treatment, data anomaly treatment and creation of the final data set with maximum level of detail possible with respect to the KPIs.
4. Analytical Modeling: Applying analytical techniques to connect these disparate data sources to quantify KPIs
• Linear Regression at the retailer-product level to attain coefficients (symbolizing the dependence of sales over the KPIs) of attributes for the time duration of specific promotions (which may be around 2-6 weeks).
• Predicting the future values of the coefficients and product-sales using a beta algorithm based on Markov Chains and Monte Carlo simulation, developed by Diamond for this purpose.
5. Recommendation Developments: findings from the data modeling to determine most profitable promotions -e.g.
• Retailer-product combination
• Geography
• Specific time of the year etc.

Although the lack of a centralized TPM system within CPG firms will be the biggest obstacle for advanced analytical analysis of the same, it’s still possible to gain valuable insights for better implementation and to improve the process on a whole.

*Sources: ACNielsen Survey of Trade Promotion Practices (2005), Cannondale Assoc’s, DCI Assessment

It appears that utilizing ATMs as a sales channel is not even a near future thing as we thought it was, it has already arrived.
Last Friday on my way home, I decided to withdraw some cash from an ATM of one of the larger private banks in India.  When I was almost done with the transaction and was about to collect the money, there was a surprise waiting for me in the screen! The ATM offered me a reasonable amount of home loan and requested me to click a radio button written “Y” if I want to avail or click a radio button with instruction of reminding me later if I want to think about it later or click “N” in case my answer was an outright no.
But then neither am I the only one, nor India the only country where it’s happening. One of our blog readers, a few continents away from my city, has already come across and availed one such offer from Chase!
Things surely are fast nowadays!

A few weeks ago, Shantanu wrote on recommendation engines and how user feedback and ratings can be a part of recommendations you provide to your customers.  But if you have ever looked through user recommendations while shopping online for a product, stock, or movie, you know that they aren’t all helpful.  Ideally, user ratings would accurately represent the population, but not all feedback is created equal, and there are some inherent challenges in these systems:

1. Not everyone will rate.  People may read the ratings when shopping for an item, but they won’t always come back to rate.  Unless a site offers an incentive for rating a product, a customer’s only real incentive for doing so is to talk about how much they love or hate it; moderates may be under-represented.
2. Ratings will be biased.  People’s individual biases produce variances in ratings, even if strict guidelines (think about employee performance reviews) are presented.  In addition, new raters tend to rate high.  Their average ratings decrease over time as they rate more items, presumably because they are exposed to more items and have a better sense of an item’s value relative to alternatives.
3. Ratings are averaged, masking the underlying data.  Because people often only rate items they feel strongly about (love it or hate it), and an average of those extreme ratings may not truly represent the actual sentiment surrounding a product.  For example, a review of rankings on Amazon.com revealed that “the reviews for the majority of the products have an asymmetric bimodal distribution. For these products, the mean of the online product reviews does not necessarily reveal the product’s true quality, resulting in misleading conclusions about the product’s future success.”  In addition, established products are at a disadvantage against new ones.  Consider a product that has received five rankings, four “5s” and one “4,” giving it an average rating of 4.8. If a new product enters the space and receives one “5” rating, it will be ranked higher than the other simply because it has fewer ratings. 
4. Ratings may be false.  Take the case of the Whole Foods CEO who posted disparaging comments about a competitor on a message board while talking up his own company, later stating “Sometimes I simply played ‘devil’s advocate’ for the sheer fun of arguing.”  Other visitors may post false comments to artificially affect the rating, and these are not always removed from the calculation.

As more products are marketed and sold online, feedback-based ranking systems are increasingly common components.  Amazon and eBay were among the first to use visitor ratings to rank products and sellers, and in February, Yahoo launched its Buzz service, which asks readers to click on their favorite stories, then uses those ratings to determine the most popular articles on the web.

Why is this important?  Because accurate product ratings help predict that product’s success, and higher product ratings lead to more sales – or, as Allen & Appelcline state, “the value of individual items (most frequently goods) rise or fall based upon the largely subjective judgment of individual users.”  So what can you do to ensure the rating system on your own website accurately reflects your customers’ views and the value of your product?

We’ve seen some thought around using analytical techniques and Bayesian mathematics to create better product rankings.  Some of the solutions explored include:

1. Adjusting ratings based on known biases.  Since some people rate higher or lower than others, one approach is to assign users a “User Optimism” value based on their rating history, and adjust the product’s overall rating based on the raters’ optimism value.  Another approach is to remove all ratings from people who have only rated 1 or 2 items, helping to eliminate new-rater optimism or “drive-by” fraud.
2. Weighting a product’s ratings based on the number of ratings received.  When a product has a small number of ratings, these ratings should count less than those for a product rated many times.  To achieve this, you can add a “magic value” into the algorithm that calculates a product’s average ratings.  This “magic value” brings products with few ratings closer to the average ratings of all products, then reduces its effect as more ratings are received, allowing established products’ ratings to float freely and more closely reflect the average of its ratings.
3. Adjusting the algorithm to account for bimodal distributions.  To account for products that only have ratings at the extremes, one approach is to use a dual-point estimation model to more accurately reflect customers’ views of the product and predict the product’s success.
4. Analyzing a customer’s rating history for fraud.  Someone who rates several products on your site should have a predictable rating pattern.  You can analyze individual customers’ accounts to identify and remove anomalies that might skew product ratings.
5. Encouraging customers to leave text-based feedback.  When a person takes the time to write out a product review, and knows that her name will be attached to it, she generally does a better job in her rating.  These reviews tend to be closer to the average than those without.

Although your customers’ product ratings may be imperfect, they can still yield insights and value with the application of a good set of analytical techniques.

Some recent academic literature has shown that companies using the standard Net Present Value (NPV) approach to calculate Customer Lifetime Value (CLV) and allocating their marketing spending might be losing substantial profits.

The standard CLV approach calculates the net present value (NPV) of all the anticipated cash flows coming in (revenues) and going out (expenses) over the projected life time for a given customer. Customers with a positive NPV are aggressively marketed and the ones which do not have a positive NPV are dropped. This is a pretty logical choice at the point of calculation- but only then. The existing conventional structure of calculating CLV is static and has the potential to leave money on the table for the company; because it does not account for the fact the company has the flexibility of abandoning a customer at any future point in time. Flexibility means options and options have a value.  The paper lays down a methodology (based on dynamic programming) to explicitly estimate the value of such options in the CLV calculation.

The authors of the paper cite the example of a specialty catalog company. They took data of 12 years and around 100,000 customers, and the difference in CLV using the traditional versus real options was as much as 20% in some cases. In fact, certain customers who had negative CLV’s turned positive when the value of real options was considered.

Retailers, communication companies and consumer finance companies who frequently use CLV as a decision making metric, are strongly recommended to read this paper.

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

A puzzle asked - “How many ping-pong balls could you fit into the room?”

Somewhere else, there was $26,000 prize for a similar question - “How many 76 antenna balls did we fit in a Chevy Trailblazer?”

And a little far away from all this - the Japanese were inventing square watermelons, probably thinking about a very similar puzzle. Or, maybe a little variant of these questions - After putting in as many round watermelons as possible, what percentage of the container space is wasted??
A little while back, Harish wrote about the impact of shelf-space allocation on brand performance and cannibalization. As I read about the square shaped watermelons that fit the shelf-space better in retail stores better, I almost went back to Harish’s post and thought- How will the sales of round watermelons dip because of the introduction of square watermelons? I am sure this product extension will displace some of the round ones off the shelf. Is round out of shape now? Or, is round being forced out of shape?

However, all said and done, assuming demand for watermelon to be static, a one for one replacement of round ones with square ones can help ­_corner_ more profits for sure!

How many times one has deleted that email offering that free gold credit card or a pre-approved loan? How many times one has curtly disconnected the phone call from another tele-caller with a pre-approved check for that awesome holiday in Hawaii? In a market, where retail banks have exhausted almost all the channels to reach the target customers, ATM machines can be a powerful alternative for cross-selling.  ATM machines also provide the opportunity to reach out to the otherwise difficult to reach customers such as expats. 

So, how would it work? Almost every bank has pre-approved offers for certain customers.  The bank can load the offers for some of the pre-approved customers to the server that interacts with the ATM machines.  The moment the ATM recognizes a preapproved customer, it can show her the pre-approved offer through a blinker or an additional screen at the end of the transaction. If the customer is willing to go ahead with the offer, all she needs to do is to press “Y”.  The moment she presses “Y”, the bank gets notified and can track the lead through different mechanisms. For example, an SMS can go to the sales manager nearest to that ATM with the customer detail, who can take up the matter further.

Surely, it’s not as easy as it sounds. While the technology for such a process is available in the market, it’s still a challenge to implement it. Prediction accuracy is another big challenge.

·         It is not feasible to upload the entire set of pre-approved offers to the server.  And hence the need for a model, which can forecast with high accuracy the probability of a customer visiting a cash machine.

·         Each ATM screen on an average takes 5-7 seconds, which includes the time taken to interact with the server.  So, the time to pitch the offers must be chosen judiciously, especially for locations with long customer queues.  But restriction of timing will in turn affect the prediction accuracy.

·         The ATM cannot close the sell. The completion of the process depends on further integrations of channels.

Nevertheless, ATMs can still be used as an excellent channel and in the near future itself, with the advent of technology, it can be expected to be one of the primary channels.