What
The 'What' dimension focuses on the Product or Service, but quickly connects to other dimensions (when, who, why). Amongst other things, useful for market sizing, understanding market share and opportunities.
Objective: Predictive analysis
[Walk]
Statistical-led predictions of what is most likely to happen. Like predicting the weather, the more data and proximity in time, the better the result. Associated maths techniques include linear regression, time series, auto-correlation, step-change analysis.
The art here is how to overcome missing macro or micro data and translating statistics into risk. Useful for examining potential sales under different price, competitor or market conditions.
Objectives: Scenarios
[Run]
Collating various predictive analysis into a model under which different marketing choices can be input to see if the outcomes will deliver the desired result.
Useful for key decision makers to plan for periods of high spend with many intersecting activities that may trip over each other.
Product: Product lifecycle
[Stand]
Where is the product in its lifecycle? How could it be revitalised. Products tend to follow a classically binomial curve which can help size future sales and strategies, and the portfolio of products can we seen in the BCG matrix.
Product: Price-point analysis
[Walk]
Looks at the relationship between prices and sales to offer input into sales maximisation or profit maximisation strategies. This gains curve is more about cost/return targeting but connects to change to the demand arc of a supply-demand chart.
Useful for understanding who will react to discount when, rather than just giving away profit. Important around peak sale periods. Can connect to changes in prices for products in at the end of life and options in a growing or shrinking market and competitor activity.
Product: Next Product
[Run]
Every consumer has an entry product and the exam question here is what will they buy next (and when). This translates into which product to offer first and to whom. I developed prescriptive propensity models such as the Next Product Propensity (NPP) models back in 2009 during my Masters.
This aspect of my NPP model is very useful at sizing product sales from customers and prospects, and sorting out the best product if there are various competing for the same market, which removes duplication errors. It also provides specific targeting of the ‘who’. Connected to Product Affinity (Product X ∩ Y /Product X) model which shows the interaction between replacement or complementary product portfolio.
Catchment: Territory Value
[Walk]
Catchment could be a town, city, province or country, whatever makes sense. Catchment could also be defined by footfall or drivetime from a specific location but adds in the profile of prospects that can be translated into product sales.
Useful for moving spend to where customers are, franchising valuation, target setting for current physical stores or planning new outlets.
Catchment: Penetration
[Walk]
Who am I targeting v who’s buying, over time. A refinement of ‘sizing the market’ to 'what is my market'. Rich seams could be soon running dry and new prospects types (or reasons) can be discovered, that may drive new or tweaked tactics. Starts with sales volumes but translates into a relative index/z-score v headroom
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