The CPG manufacturers’ most strategic growth initiative is often new product introductions. The industry, faced with stagnant sales, is gearing up for an unprecedented number of new item introductions. As illustrated below, new items introductions jumped by 13% in 2013 and double-digit increases are expected once again, in the year ahead.

New Product Intro Graph










Against the Odds

CPG manufacturers know all-to-well that roughly 80% of their new items will not have the staying power to remain on the shelf beyond the two-year mark. This high failure rate forces manufacturers to introduce even more items. By playing the “numbers game,” they expect to increase their chances of success. However, doing so exacerbates the problem by increasing failure rates proportionally.

One of the keys to new item growth is proper merchandising. While this may seem like Retailing 101, failure is most often the result of poor, or nonexistent, in-store merchandising; which of course leads to poor execution.

Beating the Odds

While the odds may be against new items, we have learned that applying advanced analytics to big data, in terms of customer purchase behaviors, provides the requisite insights for avoiding the five most common merchandising pitfalls, which are:

  1. Pricing the new product line too high, thereby diluting velocity growth
  2. Pricing too low, and compressing margins, as well as creating “trade-down” pressures for retailers
  3. Improper and/or inconsistent placement
  4. Incorrect space allocations (not capable of satisfying consumer demand)
  5. Cutting items that have no substitutes in order to make room on-shelf

Learning from Two-Billion Transactions

By applying our advanced analytical tools and proprietary algorithms to our customer purchase database, we are able to improve the success rate of new item introductions. Specifically, we analyze more than 2 billion customer transactions to determine:

  1. Price sensitivity in order to determine optimal retail pricing
  2. Price-item velocity for projecting movement and determining the requisite shelf holding power
  3. Consumer decision trees for identifying product substitutions and guiding planogram development
  4. Cluster analyses for determining purchase propensity and location recommendations

About the Willard Bishop Customer Purchase Database

The Customer Purchase Database is comprised of more than two billion transactions, from tens of millions of shoppers – purchasing nearly one million SKUs. This extensive database, combined with advanced statistical algorithms, delivers actionable insights for optimizing in-store merchandising in order to maximize growth from new product introductions.

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