Not many of us think about Amazon’s free shipping offer as a rewards program. That is exactly the point Jack is making as he classifies Amazon’s free shipping offer as a continuous reinforcement schedule. He uses a schedule of reinforcement framework proposed by psychologist B.F Skinner and applies it to rewards programs.

A continuous reinforcement schedule is dangerous. At best, the promotion shows a little lift in the short term and a huge decay in the long term (doing nothing in terms of real loyalty). At worst, you unintentionally create new business rules for your company. When those rules inevitably change, you alienate people who migrated to your company because of them. You even risk teaching users new behaviors that are bad for business

Apart from continuous reinforcement, the other reward categories are:

• Fixed ratio (SUBWAY’s “buy 12 feet, get one foot free”) and variable ratio rewards (lottery)
• Fixed interval (Restaurant happy hours) and variable interval rewards (radio contests that grant prizes “sometime this hour”)

It is an interesting framework to generate hypotheses for rewards programs, which need to be then quickly tested in the market and analyzed to determine the most financially positive program structure.

We earlier wrote about some recent academic literature on customer loyalty and highlighted the importance of a good market testing and campaign management capability to determine the optimal rewards program structure.

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.

Defining the hypotheses and doing the analysis is sometimes a lot easier compared to selecting the right graphic to present your results, which help to convey the message without confusing the audience. The multitude of charting options available in Excel does not help matters.

We recently came across a wonderful post by Andrew Abela, which very clearly lays out the various chart options and helps to choose the right one for your analysis based on the message you want to convey. It’s a must for anyone who works with data analysis.

Click on the thumbnail to see a larger image of the graphic from Andrew’s blog

One of the areas that our analytics center is working on is developing applications, which exploit the concepts of Behavioral Economics (BE), in a business context. We are looking specifically to develop a process to optimize a company’s online channel using BE concepts.

A recent WSJ (subscription required) article on Hewlett Packard mentions an interesting example which touches on two of the concepts we are exploring in our work.

1. Reducing the number of less relevant choices from websites
2. Reducing the uncertainty in a transaction process

For direct PC sales, where Dell remains dominant, Mr. Bradley reviewed customer survey data that showed H-P had too many PC models on its Web site, and reduced the number to 10 from 15. In January H-P cut the time it takes to reach a sales rep by phone to 22 seconds from 50 seconds. H-P increased its share of direct PC sales world-wide to 12.3% in the first quarter from 9.2% a year ago, according to Gartner.

We earlier posted about this topic here.