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.

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.

Recommendation Engines are being used by many online retailers today to cross-sell and up-sell products and services to online consumers. Amazon.com, Newegg.com, Buy.com seem to use some sophisticated algorithms around the recommendation engines at the heart of their online selling portal. They are also an integral part of any choice optimization strategy.

Without even going to the technicalities of recommendation engines available, we have put together a few points which can help businesses can harness the power of recommendation engines:

• Get the Basics Right
  Understanding some salient features of your products or services can help your business classify and categorize what parameters are important to track and assign weights to and make recommendations to users on. If you are a seller of toys, the most important parameters (with varying weights) may be price, toy features (learning toys or motion-based toys etc.) and toy accessories (batteries, replacement parts etc.). If you sell high-end luxury goods, the most important parameters may not include price and may just center on niche features and custom options.

• Take Baby Steps
  Unless your business is high up on the maturity curve (a la Amazon), you need not start with algorithms that are very sophisticated. Take a baby step approach to developing algorithms. Broadly speaking, recommendation engines can be classified into 3 categories:
1. Content Based filtering engines – those that are based on the contents of an item and tries to pair user interests (if known from previous web visits) to ‘contents’ of an item (can range from books to luxury cars)
2. Collaborating filtering engines or Social filtering engines – those that try to match user profile and interests with other users with similar profile and interests. Takes into account user reviews of products and services.
3. Knowledge-based filtering engines – the most complex of the three, these try to leverage knowledge of user needs and a target product or service that may not be directly related. For example, an engine may try to recommend you a GPS unit based on your interest in books related to Roadtrips or North American geography.
   
   

Ample literature is available on these recommendation engines and other emerging architectures (Few links here). The choice of what recommendation engine is best for the business should be entirely based on the business goals and the products and services being offered.

• Make them Adaptive
  No matter what the nature of the business is, recommendation engines should be built around the belief that there is never a perfect algorithm for designing them. They should be constantly capturing and storing usage data and patterns and must continuously learn from user website visits, time spent on web-pages, third party websites visited and so on. Every time the same user visits your website, the recommendations that are presented will be a little more relevant than on the previous occasion. A sample starting point architecture can be found here.


• Keep it True
  Last but not the least, objectivity of recommendations is valued by the savvy consumer. An online platform for freely expressing opinions (on user experience or product performance) can feed into your recommendation engine to make it better with time. Amazon.com and Buy.com provide not only good platforms for customer feedback but also opinions on where else shoppers can get the same product they are looking at cheaper or costlier rates. The neutrality of the ratings and objectivity of the price positioning influences a customer positively and presents an image that these sellers are not trying to peddle their wares necessarily, but want you to make a good purchase decision as per your needs, preferably through their own stores but not necessarily.


Photo Credits: ieatcrayonz, scott92007, Jmb, losiek

Webmetricsguru points to two interesting studies done on impulse buying. One of the studies uses a behavioral economics framework (with brain scan evidence) to explain the buying process.

..consumers trade off the immediate pleasure of making a purchase against an immediate pain: the pain of forking out the money for the item.

Furthermore, to increase the likelihood of ‘impulse’ buys, online merchants are suggested to follow some principles which are very analogous to the ideas we have put forward in our recent Choice Optimization paper.

1. Expose the target audience to the “stimuli”:
   Priming the audience through targeted advertising and product placement
2. Figure out what the price point for buying “stimuli” items are for the target audience(s):
   Avoid price risk for consumer through market research, transparent pricing (showing competitor prices like Progressive) or price matching schemes
3. Offer the item(s) (stimuli) at a lower price than what the target audience thinks it’s worth:
   Relative framing by comparing the value of the decision compared to an alternative.

To make the point in tangible terms, here is an interesting example of relative framing which we found while researching for the whitepaper:

The Economist magazine once offered its three subscription options on its website,
(a) Online only for $59
(b) Print only for $125
(c) Online and Print for $125.

16% of customers chose the “Online Only” option and 84% selected the “Online and Print” option. No customer chose the “Print Only” option, so the company removed it. However, when only two choices were presented (“Online Only” and “Online and Print,” at $59 and $125, respectively), the number of customers choosing the lower priced “Online Only” product increased to 68%, while the percentage of subscribers choosing “Online and Print” dropped to 32%. While no one chose the “Print Only” option, having it available made the more expensive “Online and Print” option appear to be a bargain, and this drove a higher percentage of customers to select it.

Photo credit: PaysImaginaire

Professor Dan Ariely of MIT has done extensive research on human decision making using the framework of behavioral economics (go here to pre-order his forthcoming book on the subject). We recently collaborated with him to apply some of his research ideas in the online world. The result of the collaboration is the white paper: Predictably Irrational Customers: Optimizing Choices for How People Really Buy, Not How We Think They Buy.

As customers begin to make more financial decisions online either by conducting a transaction or researching a product online before buying at a store, it becomes critical for companies to have framework to understand customer decisions to generate potential ideas to improve the website. This coupled with the ability to rapidly baseline, test and evaluate the ideas using web analytics makes for a potent capability for companies to gain competitive advantage.

You can download the white paper here

PS: You can also set up sometime to speak with our resident choice optimization experts by sending an email.

Ron (commenting on Larry’s post) identifies loyalty program as a key reason for the holiday season success of online channel of retailers. Apart from driving sales, loyalty programs generate valuable data which can be analyzed to determine tactical strategies like cross-promotional programs, estimating discounts on bundles etc. Here is a four step actionable process to exploit the loyalty card data, and use it to broadly target customers with effective offers, even those who are not members of the loyalty program.

Step 1
Append an off-the-shelf segmentation schema (such as Claritas Prizm NE or Acxiom Personicx) to the loyalty card customer data (matched on name and address information) to get an understanding of segments of your customers and associated demographic and lifestyle behavior

Step 2
Identify cross-promotional opportunities and determine optimal discounts by using the purchase history data of loyalty card customers to determine which products are bought together by customers (product correlations in the market basket). Use data from past promotions to identify customer’s sensitivity to discounts (paying full price vs. buying at maximum discount) and overall volume and profitability of bundles. A simple visual will show a set of product associations (at various discount levels) helping to identify products that can be bundled. Green boxes in the diagram below represent recommended bundles.

Step 3
Using the augmented loyalty data identify customers who purchased the recommended bundles and build a propensity model to score segments based on their likelihood to purchase the bundles. Rank order the segments based on their propensity to respond to particular offer bundle.
Step 4
Integrate this segment ranking information into your online and direct marketing targeting schemes.

Now, not only do you know what to bundle, but also which members to offer the bundle within your loyalty program. Since you have used an industry standard off-the-shelf segmentation schema, you can append your prospect list with the prioritized segment information and more effectively target even the non-members.