When you’re about to spend money on something, even if it’s just $10 for a hamburger, you want to know in advance what you’ll get in return. Datamine is often asked about this value exchange when in discussions with clients about proposed analytics projects and scopes of work - before they jump in, people want to know how much better off their business will be if they pursue a given initiative.
Now figuring out value for money with something like a hamburger is straightforward - you pay $10, and the ‘value’ you get in return has to do with deliciousness (and maybe size) of the burger. The value you get with analytics, on the other hand, is much more challenging to predict because it’s dependent on factors like the following:
- How much capability your organisation has
- How much analytics your business has done before
- Whether or not people internally are on board
- How the results of the project will fit in with existing infrastructure
While there are many factors that can influence the potential outcome and value of an analytics project, the biggest one is usually implementation. Once we’ve worked with a client to identify the problem and a proposed solution, their first reaction is often a desire to get started right away...and while we’re thrilled that clients are excited about the value analytics could bring their business, it’s incredibly important to first make sure the framework that will enable the initiative to be successful are actually there in advance. This is because it’s rarely just the analytics project spend that brings value to a company - it’s the surrounding business processes that ‘activate’ that spend and make it valuable.
Let’s say your marketing team is constantly stretched for time and you want to implement a marketing automation programme to make things more efficient - great! But is your customer data clean and up-to-date? Do you have target personas and segments to enable personalised messages? Do you have all the data in one central and usable place? Does the team even have the time or desire to onboard a new technology? If any one of these surrounding processes isn’t in place, you could very well spend hundreds of thousands on something that has the potential to bring radical value to your business, but only ends up making you poorer and more frustrated.
So if you want to know what you’ll get out of X analytics project, the answer is this: it depends on whether or not you’re able to draw a line of value between the following steps:1. Pinpointing the problem that needs solving
2. Determine what the analytical solution is
3. Figuring out how you’ll capture the benefits
Part of ‘capturing the benefits’ is making sure you’ve paved the way for that analytics project to be successful. If your business has identified a problem that could be addressed through a certain analytics solution, you then need to outline exactly what will need to happen for the results to be implemented - and if there’s a roadblock somewhere along that process, it needs to be dealt with or streamlined before you begin work on the ultimate solution.
Analytics has the potential to bring an enormous amount of value to businesses that are able to implement their results. Here are a few case studies outlining the successes some of our clients have seen through analytical work:
Case study: New Datamart delivers retailer millions in extra sales
Although a large retailer had an extensive customer base, it was unable to effectively utilise its value as customer records were housed in disparate systems — preventing easy access or leverage.
Although the retailer had a large amount of customer data, it was not centrally located or configured for marketing analysis. To address this issue, Datamine created an analytical ‘datamart’ (essentially a subset of an overall data warehouse), comprised of multiple source systems which tied the customer information together — providing a strong platform from which to drive communications.
The $150k investment was recouped easily by the uplift in sales on the very first day the Datamart was used for targeting. In conjunction with additional analytics and capability supplied by Datamine to bolster the internal team, this retailer’s data mart has subsequently delivered well beyond expectations — generating tens of millions of dollars of extra sales.
Case study: Out-of-stocks - what you don't know can hurt you
Anecdotal evidence at a large retailer indicated that out-of-stock issues were potentially having an effect on sales, but justifying additional investment into distribution was difficult because the lost revenue from out-of-stocks could not be quantified. The retailer had tried and failed to perform these analytics using supply-side information.
Datamine was asked to examine the problem and by using demand-side analytics was able to provide a good estimation of the cost of out-of-stocks at a product and store level. This not only helped with the investment decision, but also had the added benefit of highlighting the significant disparities in operational effectiveness between different stores.
The results of the double benefit were seen quickly as the $80k analytics job rapidly delivered a net benefit of several hundred thousand dollars per week by improving stock availability and out-of-stock processes.
Case study: Major insurer finds gold in its data siloes
A large multi-national insurance company had concerns about the efficiency of its marketing efforts and approached Datamine to request some ‘exploratory’ work be done. The company wanted to establish the possibility of extracting data from its array of different systems — data which could then be applied to better inform its strategy around brand expansion and customer propositions.
Datamine brought together a host of different systems and data silos into a single datamart – enabling a single customer view and identifying multiple opportunities. One entirely unexpected result was that Datamine uncovered millions of dollars in premiums which had not been charged, even though the policyholders were still being provided full cover.
The $80,000 project returned almost $10m to the company immediately — and provided insights which led the insurer to establish an additional brand and associated product range in the marketplace, while simultaneously improving its foundation business through better targeting and messaging.