Analytical Engine

We recently interviewed Bill Dorion, who is the Director of IT at PepsiAmericas. He has been a pioneer of championing and executing key analytics initiatives for PepsiAmericas. Bill has been in the Information Technology sector for 30 years, which includes 22 years in the CPG industry. Bill, in his current role, manages IT applications development team for strategic solutions selection, development and implementation.. Reproduced below, are some of the excerpts from our conversation with Bill.

Question: Can you tell us something about the type of problems you are solving in the organization using analytics?
Bill: Given our current strategic priorities, we are using analytics for reducing out of stock at retail locations. There are three key components that we are focusing on:
• Better Inventory forecasting (having the right product at the right delivering location)
• Divisional Dispatch & Voice Pick (having the ordered product picked and delivered correctly)
• Suggested Order Solution (ordering the right product and quantity at the right time to fill the customer demand)

Question: How do you quantify the value realized by an analytics project?
Bill: Analytics has several quantifiable and measurable benefits for us, as we have realized during the learning process:
• Reduced out of stocks at retail locations
• Reduced inventory levels are at the retailer back room
• Optimized delivery schedules suitable to better meet demand
• Less time for sales reps. in the ordering process and more time focusing on selling

Question: What are some of the key challenges that you face in analytics focused projects?
Bill: PepsiAmericas did not have a lot of experience in handling analytics projects which presented us with some challenges. The key learning for us was to understand that analytics projects have a strong R&D component inbuilt into them. While our organization is very comfortable with execution projects, the test and learn cycles inherent in an R&D process took some time to get used to. Another challenge for us was selecting the assistance of right resources that bridge the gap between business, statistics and common sense, and can communicate between these groups effectively.

Question: How do you see analytics growing within your organization and within the CPG industry?
Bill: If you ask me about the present, I see analytics growing in two main areas, optimizing the ordering process and helping in pricing and trade promotion spend

Question: Since selection of the right analytics resources is a key component for the success of the project, what do you look for in an ideal analyst that you hire into your team?
Bill: From my perspective, there are three key ingredients to a key analyst hire in our team. First and foremost, the analyst should fit into the culture of the team . He/She should be good at understanding business problem and incorporating it into the statistical tools to solve the problem. Last but certainly not the least, effective communication skills are very important. I talked about it earlier when I mentioned the need to bridge the gap between business, statistics and common sense through effective communication.

Mobile phone operators are facing a rapidly saturating market, thus facing customer retention as the primary challenge.  Mobile operators, like almost all big retail operations, have their own predictive churn models. Moreover, mobile operators have the luxury of having a wider array of variables. The “Know Your Customer” questionnaire and the usage behavior of the mobile phone typically provide access to a few thousand variables.  Modelers frequently add some off-the-shelf segments (from providers such as Claritas or Acxiom) in the mix to make a better sense of and add more predictive power to the model.

But the large number of variables adds at least two complexities.  First, even after performing variable reduction, modelers usually are still left with a high number of components/ factors, a number high enough to make a concise business presentation difficult.  Second, the high number of variables also affects the actionability of model insights.  No single variable affects churn in isolation, so it is hard to determine the independent impact of individual variables in customer churn.

An alternative approach to comprehend and act on the model recommendations will be to bundle the variables in logical groups.  For example, the variables related to time can indicate when a customer is more prone to churn and can be grouped as a ‘when’ group of variables. This ‘when’ group will not necessarily tell us why a customer is more prone to attrition, and would rather tell us when she is more prone to attrition.  Similarly, the variables’ indication of issues faced by a customer (her interactions with call center might be a good source for this) such as handset problem or network issues can be grouped as ‘why’ variable.  The demographic variables that are significant in predicting churn help the modeler to profile the customers likelihood to churn and can be grouped as ‘who’ variables. An integrated view of these three groups is more palatable, and from a modeler’s perspective, it is easier to convey to the business ‘who’ are more likely to churn, ‘why’ they are more likely to churn and ‘when’ they are more likely to churn.

The above approach does not only make the models easier to comprehend, but also increases actionability by providing a structure to the model findings.

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 - 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 - 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!

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.

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Societe Generale has been in the news recently and not quite for reasons they would like to be in the news for . In one of the biggest cases of Insider Trading Fraud, an SG employee managed to cause a dent of over $7bn. And this is hardly the first ever case of such use of material non-public information. In one of our earlier posts, we talked about a project where we had built filters to tag suspect insider trades to help our client with identifying potentially suspect trades for manual evaluation. The system has been used retroactively so far, and is being considered for proactive tracking as well.

The efficacy of what we proposed was based on the simplicity of filters that we built. Using tribal knowledge for identifying suspect transactions, and augmenting it with statistical rigor and tests, we were able to generate efficiencies in the compliance/audit process. These same rules can be implemented in a real time fashion to flag suspicious activities for monitoring.

Update: Julie wrote a note to us and pointed out that SoGen case is not a case of insider trading but one of outright fraud. Thank you for catching it and pointing it out to us. Nonetheless, a set of logical and statistics based filters can be a useful part of effective control environment which may have been able to detect such fraudulent activity.

Investment Banks, with activities encompassing trading as well as deal making, are susceptible to information leakage across activities.  Traders often have access to the material non public information (MNPI) i.e., insider knowledge from the deal makers, which if used can generate significantly higher (illegitimate) profits for the trader or the employer. In recent past, a few traders from leading investment banks have been found guilty of using MNPI and punished. It is beneficial for the banks from a pecuniary as well as reputation point of view to track insider trading activities and take actions against offenders. 

However, it is difficult and resource consuming (financially and intellectually) to manually eyeball millions of trades done by employees and flag potentially fraudulent activity. What may help, though, is a set of robust logical and statistical filters that are designed to reduce the overall number of trades/traders subject to manual review, thus reducing the overall cost of regulatory compliance.

The approach described above requires a non-trivial effort up-front to acquire, aggregate, and clean the trading data. After the required cleaning of trades’ data and imputation of missing value of trades in certain time periods (assuming no trade), we build a general understanding of the traders’ behavior. We supplement this understanding with the help of logical/statistical filters which are a combination of basic measures (such as directionality of trading transactions, means and variances of trade values), trend analysis (such as correlation between trade and market movements, deviation from trader’s historical trading pattern) and statistical models (such as time-series models).

 

Fig 1: Example of correlation between market movements and trading
action

 

Fig 2: Example of analyzing directionality of trading actions

 

Some of the complexities of this approach lie in the inherent biases in any technique that the analytics as well as business teams should be aware of. Whether an OLS model will work as well as a time-series model to identify suspect transactions is a hotly debated question, for instance. The key to this problem, as in most of the advanced analytics
problems, is a strong coordination between business and analytics. Subject matter expertise around investment banking, trading, stock price movements, etc. must be built into a data-driven approach to solving the overall business problem.

If you want to develop an analytics capability within your organization – should you be talking with business intelligence software vendors or data analytics service providers?

Jeff Kaplan in a recent article in DM Review makes a case for the latter.

Delivering predictive analytics is not a trivial exercise. It requires the skills of being able to map the marketing goals to the appropriate predictive algorithms, perform data hygiene and transformations, build models and test the results.

Moreover, implementing predictive analytics requires the combination of three distinct skill sets:

1. database technology
2. data mining and
3. marketing domain knowledge

He goes on to identify executive support and expertise of the partner (in understanding data, statistical modeling expertise and database skills) as the two success factors of such initiatives.

My take: The principle of service before software in analytics makes sense because of the complexities that Jeff identifies (see related posts on the topic here and here). A service first approach also helps to rapidly identify the specific areas within the organization where analytics will be a quick hit due to data availability, quality, and acceptance of analytics within the group.

Another aspect which we have found to be critical in the success of analytics in the organization is being able to generate the right set of hypotheses for the analysis. The hypothesis driven approach helps an organization to focus on the problems that matter. A service provider, from prior experience, can help to generate a long list of hypotheses. However, in our experience it is critical to identify and recruit savvy individuals from within the organization, who have deep understanding of the day-to-day business operations and are passionate about improving the status quo with better analysis of data. They are able to pick out the specific hypothesis and analysis that can help the group to make better decisions. We call them ‘insight managers’. Normally, they become the analytics champion within the group and are vital to its broader acceptance.

Diamond has developed a model to deploy analytics within an organization and ‘insight managers’ play a major role in it.

Heinz recently reported a very successful year driven in large parts by its pricing strategy. Increasing prices and simultaneously gaining market share is the holy grail of Consumer Packaged Goods (CPG) companies. A 2005 report from GMA, Nielson and McKinsey identifies some of the salient points around sales, pricing activities of ‘winning’ companies (which they define as companies who not only increased its pricing compared to its category peers, but grew their segment dollar share at the same time)

Setting Price:

• Manage pricing regionally (and some at the market area, micro-market, or even store level) to reflect local variations in price sensitivity that arise due to differences in consumer preferences, competitive intensity and retailer dynamics.
• Use more price elasticity analysis to set the price and integrate consumer insights into pricing more aggressively
• Tailor brand-pack assortment to the needs of a particular region, channel or store format

Realizing the Pricing Increase:

• Use a profit-based rationale to secure price increases and also combine this tactic with a strong understanding of consumer preferences, shopping behavior, and price elasticity.
• Appoint a single head of pricing who collaborates with top leadership and critical functions on both the pricing strategy and its execution. An essential part of that person’s task is then translating the strategy into sales force guidelines that help avoid conflicts between profit and volume goals.

In short, the winning companies are using analytics to drive their pricing decisions and have gone beyond the simple volume based pricing strategies that is the norm in the industry. Profitability, consumer insight, channel management and price elasticity are all areas which require data analytics capabilities. The end of the ERP implementation cycle in the industry means that companies will start developing predictive analytics capabilities and try to use information and analytics as a sustainable competitive advantage.

Customer risk segments are used to identify relevant product offerings. However, it seldom acts as a key service differentiator. Focusing on customer risk segments, especially as a grievance redressal tool, can help break down the century old cycle of (1) investigate, (2) verify, and (3) process the transaction. This three step procedure proclaims the customer guilty even before the verdict is out. Reengineering the resolution process can turn dispute resolution into an opportunity to build relationships.

When a customer disputes financial transactions, banks start internal investigations, verify the customer’s claims and then reimburse/waive-off the transaction amount or reverse the financial cost that has been levied on the customer. All these happen only if the customer claim is found to be true. But what about the grey areas where the verification process could be inconclusive?
Well, most retail banks have exception handling policies. Impressive!! Just that they are implemented long after the customer is fuming with anger. Is the customer going to be satisfied? The answer seems to be an emphatic No.
The three step resolution procedure can be strategically changed with the customer being “trusted” first, and the investigation, verification steps being done in the background. To implement this, we need a customer risk score available to all customer touchpoints (phonebanking, internet banking, etc.). The risk score identify customers that can be trusted.

Expertise in building  robust scoring models and process re-engineering can help identify process pain points that need correction. This can be leveraged to reinforce the relationship the customer shares with the bank, thereby increasing the lifetime value of a customer.

Two useful links if you are in the market for web analytics solutions.

Pat McCarthy has put together a great summary of all the major web analytics solutions (both free and paid) available in the market. The list includes all the usual suspects and some niche solutions as well.

Avinash in his insightful post recommends a radical process of choosing a web analytics solution for companies. Instead of following a major requirement gathering exercise followed by an RFP, he recommends implementing a free tool, deepening the analytics skill set in the organization, fixing the internal data capture issues before starting the RFP process.

Incidentally, Google Analytics (which is free) is the solution of choice for the websites of the most of the current Presidential candidates websites.