1. WHO ARE YOUR 'BEST' CUSTOMERS?
Where can you find more of them?
Logistics firm wanted to expand their network of parcel lockers
A logistics company wanted to overcome the hurdles of The Last Mile - the ability to actually deliver a package to a household or business. When no one was at the address to accept a parcel, another delivery time had to be arranged. Not only was this inefficient for the company, but it was also frustrating for the consumer.
Their solution to this problem was to set up a bank of lockers to which consumers could have their parcels sent. Their initial trials were in inner urban areas filled with young, hip, time-poor professionals.
We analysed their data and found that the largest group using the lockers - about 25% - were indeed the very target market they went after. However, the target group made up nearly 33% of the area's immediate population. The target market didn't love the solution. The logistics company badly under-indexed with them.
Families with young children were the ones who really loved the concept. They made up 10% of the users, but accounted for only 3% of the population. The reason for this was that these families are not just time-poor, they are time-inflexible. They have a fairly strict routine (school drop-off/pick-up) and they just can't be at home to accept a package or go to a depot to pick it up.
Had the logistics company set their roll-out strategy based solely on the target market (the ones who meant the most to the logistics firm), they would have put in place actions that would not have led to good outcomes. Tailoring strategy to the people who love you delivers better results to your customers and better returns to you.
fmcg company wanted to grow beverage sales
A large, international FMCG company brought a successful sports drink into Australia. Initial sales, unfortunately, did not meet expectations. While the company only had sales figures from one of the major supermarket chains, we were still able to identify key locations and key consumer targets.
The first step was to 'normalise' sales. The top supermarket outlet in the country was in the Sydney CBD. This was not surprising as this is one of the top grossing supermarkets in Australia. In order to assess properly who loved the beverage the most, it was important to put sales into context. In this case, we looked the beverage's sales as a percentage of total beverages. The ratio itself is not that important, but rather how each supermarket ranked against each other and the magnitudes of difference.
The results showed that the Sydney CBD store was in fact an average performer. It's gross sales were the result of a high turnover store and not because the customers at that location really loved the product. The stores that the true top performers were mostly in areas that were adjacent to industrial areas - Clayton in Victoria, Port Pirie in South Australia, Canning Vale in Western Australia and Port Kembla in NSW.
While the marketing was aimed at hip, triathlon-running, super-active 20 to 34 year olds, the people who really loved the product were the factory, smelter and industrial workers who were using the product to supplement their strenuous jobs.
The promotions and distribution of the product needed to be altered significantly in order to serve better the core users and thus to grow the product to its fullest extent possible.
2. WHICH ARE YOUR 'BEST' LOCATIONS?
WHERE CAN YOU FIND MORE OF THEM?
FINANCIAL SERVICES FIRM WANTED TO BETTER UNDERSTAND ITS NETWORK
A major financial services institution thought they knew their market share, by postcode, fairly well. They assumed that their share of households in an area was equivalent to their share of mortgages, other loans and deposits. Based on a request from the Board, they needed to investigate the numbers accurately.
Needless to say, the company didn't really know it's place in the market as much as they had assumed. They knew their customers and they had great data, but they had never put their data into context before. Once they knew their market share for the various products and not just assumed it, they were able to put into place strategies to grow successfully. This is what we mean by looking at the Data to Wisdom to Action continuum.
One reason they had never calculated their market share by postcode was that the data for the total market at that granular level is not published by the Bureau of Statistics, the Reserve Bank or any other agency. The first step then was to create this data. Working from our dataset on potential consumer expenditure, the national benchmarks from various governmental bodies and various surveys and datasets from the ABS, we were able to come up with the market size, at the neighbourhood as well as the postcode level, for a wide variety of financial products.
With these pieces of external data giving context to their own data, the company could see that the areas in which they had a high proportion of households as customers did not always translate into a proportional share of financial products.
This wisdom was extremely helpful in formulating meaningful actions which led to much improved outcomes.
entertainment company wanted to know areas for expansion
An entertainment company wanted to understand where in Australia there were areas that were under-served and where there were areas that were un-served for their product. The Board wanted to know whether the country was saturated as this would then inform the scope and extent of capital expenditure needed over the next five to ten years.
We were able to show the company that there were close to two dozen potential sites across the country that were under-served. There were two key components to this work. The first was to assign a level of supply to each neighbourhood in Australia. The company's existing locations varied in size, but were generally located in major shopping centres. Thus, the first part of the exercise looked at the catchment, allowing for the fact that people living closer to the site were more likely to visit more often and that large and/or more successful existing locations would have a stronger and wider reach.
The second part of the exercise then looked at demand. Working with the company, we were able to profile the key demographics driving demand and could then calculate the total demand for the product from each neighbourhood.
The final step was simply mapping the results to see which under-/un-served areas had a large enough population to warrant investment in a new site.