The Importance of the 4P’s
When we bring products to the marketplace we develop a marketing plan across the 4P’s. Some marketers believe the 4P concept is old fashioned, but we’ve found them to it to provide a very robust framework to understand the key drivers in the marketplace. When we bring a new product to market we begin by developing a product with the right functional features. Through our promotional messages and use, we and our consumers add emotional (brand) attributes to it. Then we sell it at some price in some distribution channels. In some channels it might be offered at a higher price (e.g., Drug). In others it might be sold at a lower price (e.g., Mass, such as, Walmart). Then we provide messages into the marketplace about the product. We hope to make our target consumers aware, generate purchase intent and hopefully, influence the perceptions about the brand. All the 4Ps work together to deliver the products in some fashion, compared to competitive offerings to the consumer. Brand and product managers try to optimize this mix, either thru gut feel, shooting from the hip, or through analytics to estimate the impact of their actions in the market place.
In order to determine the best marketing mix, we need to look at all our actions and those of our competition and the 4Ps provides a great framework for us to do that.
Most major brands have begun to use a few different forms of marketing analytics known as Marketing Mix Modeling and Agent-Based Modeling to help to support their decision-making processes. Both of these methods have their place in the toolkits of modern marketers, offering value, not only to determine past ROI but also to optimize future success. As these two methods become more mainstream many of their differences are becoming apparent. Some of these include:
- Statistical Marketing Mix Modeling is close to Last Touch Attribution
- Single brand model vs. category model
- ROI measurement vs. future success optimizer/simulator
- Data periodicity limitations
- Media centric vs. consumer centric
- Uni-dimensional vs. multi-dimensional optimization
In this three-part blog I will discuss these key differentiators to provide information as to how to improve marketing analytics through a new methodology, namely agent-based modeling, and specifically through ProRelevant’s MarketSim.
Statistical Marketing Mix Modeling is close to Last Touch Attribution
We see a progression of methods of marketing measurement to help marketers understand what works and what doesn’t. One of the early methods and one that continues to be used, is Last Touch Attribution. Through some sort of direct attribution, such as a promotion code the full weight of a media activity would be attributed to a specific media channel. Coupons represent a good example of Last Touch Attribution where the coupon code refers back to a specific coupon mailing. Unfortunately, Last Touch Attribution ignores all prior and concurrent touches. It ignores the value of trial and repeat as well as the incremental value of the brand.
Marketing Mix Modeling using statistics operates in a similar fashion, except the direct attribution is made through a statistical regression analysis. In most statistical marketing mix models, the value of the brand and the prior experience with the brand is ignored by the modeling method.
MarketSim Agent-Based Modeling includes these aspects, because it incorporates past experience and it incorporates a choice model to provide the selection that a consumer makes at the moment of truth.
Stay tuned. The next blog post will be on its way in a few days. Please join us for our webinar on this topic on July 28th.