Take your brand strategy in FMCG beyond guesswork, utilising a range of available datasets to increase shopper loyalty and boost profitability.
A large FMCG chain aimed to:
Datamine transformed transactional data into actionable marketing knowledge by:
This was done by identifying households and their ‘shopping basket’
Using data analytics to generate critical marketing information about each household, including:
This customer insight was delivered via a secure online dashboard and key management reports were provided.
The analysis identified the top segment of the retailer’s customer base in terms of spend and profitability. The customers within this segment were shown to be more likely than the base to:
Although this segment only accounted for 8% of the retailer’s total customer base, they accounted for 29% of its total customer spend. As these high-value customers were already spending, the retailer focused on providing more relevant and timely offers to this group — and achieved greater promotional response rates and a significant return on investment.
New World wanted to improve its advertising effectiveness by reducing the number of circulars distributed – without impacting store revenues. The objective of the project was to answer the following questions:
Transactional and Census data was extracted to calculate the profitability of each geographical meshblock. Unprofitable meshblocks were identified by comparing the profit from the sales in the meshblock to the cost to distribute circulars.
The impact on New World was assessed by the use of control groups and testing the approach in similar catchment areas. Profitable meshblocks were then converted into distribution walks.
Following the results of the analysis, New World decided to stop delivering circulars to unprofitable meshblocks in order to focus on valuable areas.
Distribution at the time was systematically reduced, with six digit cost savings in the first year and no adverse effects on chain revenue.
A major FMCG group were running full page newspaper ads showcasing discounts at one of their supermarket brands. This type of advertising is expensive and the company wanted to assess its short-term return on investment and discover whether or not the ads were actually impacting sales.
Datamine used supermarket checkout data to analyse the impact of the newspaper ads. Sales in the promotional periods were compared to before and after the promotions and to those during the advertising period.
Datamine also assessed the effect of the product specials themselves, measuring increased sales of the advertised products and the revenue change of products within the corresponding category.
The analysis found no significant increase in sales revenue or store visits during the promotions. In line with this, there was no evidence that the newspaper ads affected customer behaviour.
This data-driven strategy has assisted the company in optimising future media planning and ensuring the best return on investment from its campaigns.
Consider the following:
Fact one most companies are in business to make a “profit”
Fact two most companies derive value from customers
Fact three few companies know the profitability or relative value of individual customers, products, or channels
Competition in saturated markets being as fierce as it is today means it’s vital that businesses know the profitability of customer segments, individual customers, different products and services, and different channels of distribution.
Datamine’s client, a wholesaler of FMCG, wanted to analyse the net profitability of individual customers in order to focus marketing activities on its most profitable markets and customers.
Datamine takes a practical approach by approximating value to give relative profitability, or provide profit at a very precise level.
In this case, Datamine merged the customer billing and cost databases to calculate the gross profit for several million retail customers purchasing more than 100k different products.
Datamine then took historical customer transaction data and evaluated the activity based costs of supporting each individual customer across three different sales and service channels.
The resulting net profitability analysis enabled Datamine's client to identify and focus marketing initiatives on those customer groups and market opportunities providing the highest profitability to the company.
The client gained a new understanding of the value of products, services, channels and customers in relation to each other.
This enabled them to see where value or profitability can be improved, identify those customers who are not worth actively retaining and take a step toward determining the lifetime value of their customers.
In addition to helping focus marketing campaigns, the analysis can drive development of new products and services based on their likely profitability across all customers as well as identifying which activities are adding value to the business, those that do not, and those that are business sustaining.