The newest, and perhaps most powerful approach to achieve optimization is through . As with many “hot” technologies, has come to mean different things to different people.
For the purposes of this article, we will use the Wikipedia definition: “machines that mimic ‘cognitive’ functions that humans associate with other , such as ‘learning’ and ‘problem solving.’”
Many have already experienced early forms of when making purchases on e-commerce sites such as Amazon. After making a purchase, customers continue to receive “suggestions” for weeks after the purchase. Some suggestions are on target, but many are not.
Big data and analytics, combined with , already shape decision-making for retailers, and this influence will only continue to grow. Because of tremendous competition to attract and retain shoppers, combined with the competition to gain share of voice through digital marketing programs, harnessing to optimize every detail of a campaign is a must-do. The good news is: Enormous datasets are already available to CPG marketers and are ideal for applying .
will impact and accelerate three major activities involved in retailer marketing:
Reduce human time involved in skillful and complex tasks associated with work such as modeling, nondiscretionary variable selection and testing multiple campaign scenarios.
Introduce the practice of dynamic modeling decision triggers that are automated and driven by market changes versus contractual or cyclical demands.
Move away from user-driven individual war-gaming to -powered “smart simulations.”
- Shopper data – in-store purchase, shopper behavior, income, lifestyle interest and related information.
- Audience data – penetration, buy rate, buyer flow, new/loss/retained and similar information that helps marketers identify high-value shoppers.
- Media data – which medium/media shoppers prefer, such as mobile, email, video or TV, and when/where shoppers’ consume media and more.
- Measurement data – determining the metrics for each medium; e.g., TV planning will measure improvements in viewership, while digital campaigns will focus on evaluating efficiency among publishers.
uncovers discrete shopper segments that can prove to be high value, as well as facilitate modeling and scenario testing during campaign development.
Retail marketers today can access one-to-one, highly granular verified purchase data that provides a large amount of detailed information about the shopper, such as lifestyle preferences and price sensitivity. Verified purchase data provides an abundance of information on current, lapsed or decreaser buyers based on frequent shopper program (FSP) data from hundreds of thousands of households.
complements verified purchase data by identifying detailed nuances about shoppers similar to those who have made actual purchases, identifies additional needs current purchases may have as well as recommends strategies for lapsed or decreaser shoppers.[…]