FitFarm: Marketing analytics boosts a more holistic customer understanding
There are a couple of challenges in utilising analytics that are repeated in almost every new project. One of these is to obtain data from the actual data source and format into a form that can be utilised by analytical tools.
Another challenge is how to get the data to move through the desired intermediate steps reliably, but if necessary, changes can be made quickly, or new data sources can be added. There are certainly many other challenges, but in practical work these require a relatively large amount of time and effort and do not in themselves add value to solving the actual business problem. After all, solving business problems is the reason why analytics actually pays off.
There have been good developments in technology in this regard. Through cloud services in particular, many of the tasks that burden IT staff are handled cost-effectively and without upfront investment, allowing new solutions to be tested on a smaller threshold. Especially for smaller companies or those that do not have an actual analytics solution, this has not been easy in the past, and projects have become heavy in terms of both cost and duration.
We have strived to find the best products in this area, as well as to build processes that make the entire solution cost-effective, reliable and flexible according to future challenges. The aim is to keep the customer actively involved in the process so that maintenance can be carried out independently, without outside help.
As one example, we have implemented analytical solutions in the field of marketing, which make it possible to make better use of e.g. data provided by social media channels and to combine it with data from companies’ other systems. In the integration, we have utilised existing services, such as Stitch Data, with which we can easily obtain data from several different services in a suitable format. Amazon Web Service S3 works excellently as a data storage platform, and we can utilise data from there via AWS Athena to Tableau.
In marketing analytics, data sources can include social media and advertising platforms, web page traffic and e-commerce. Combining and visualising data sources in a single dashboard allows for a more holistic customer understanding.
A solution like this has been implemented with our customer FitFarm. FitFarm's CEO Eino Tuominen has been actively involved in the project, and Markus Ylikojola asked him about his experiences with this project.
What kind of company is FitFarm and what does it do in practice?
Eino: FitFarm offers online coaching in exercise, diet and general well-being. So, no correspondence courses, instead, the coach is involved throughout the process. This has been done for ten years, the first company in Finland in this field. More information on our services can be found on the FitFarm website.
FitFarm enhances customer understanding with marketing analytics
Many companies start utilising analytics with financial data, such as accounting materials. You, however, started with marketing analytics. Why is that?
Eino: Often companies start analytics with financial data. However, the benefits are marginal compared to the actual operational data. After all, financial data is the result of actual operational measures, the effectiveness of which should be primarily measured by analytics. The consequences of these manipulations are then reflected in financial data.
Our goal was to find a solution for marketing, which utilises several digital marketing and e-commerce data sources. In addition to this, there had to be an opportunity to increase these sources in the future, especially as we move beyond digital marketing and start measuring other issues. However, few companies seem to have this type of marketing solution yet, even though the need is very similar.
How long did it take to build the solution? What else did the project require?
Eino: It took a few months in calendar time, but the actual building took about a hundred hours. In addition to this, it took time for FitFarm to think about what we want to get out of the system as the outcome. The customer's active role in the idea of what can and should be achieved, as well as familiarisation with the topic even before the project are important prerequisites for success. The literature and online materials on the subject have been good sources for being able to discuss ideas with the supplier that could be put into practice.
How successful was the implementation?
Eino: The implementation as a whole went pretty much without problems, at least from the customer's point of view. Entering parameter data related to certain products is actually the only thing that occupies us a reasonable amount, but everything else now works quite automatically.
How would you assess the benefit-cost ratio? Was the outcome worth the effort?
Eino: We reap immediate benefits because we can implement more specific marketing measures in a timely manner for the optimal target group.
Does the solution allow for something that was not possible before?
Eino: In theory, the same could be done by hand, but in practice the amount of data required for customer understanding is so vast that it cannot be controlled manually. Combining multiple data sources is the key to achieving customer understanding. Similarly, there will be new and interesting opportunities for understanding various phenomena, such as the effect of seasonality, in the future.
How has the agile model followed in this implementation worked in this project?
Eino: The model has worked really well, because FitFarm's needs were well understood by the supplier, even if they were sometimes related in quite complex terms. Everything was understood correctly right away. Working on a simple model has also given rise to ten new questions and ideas, which would also be nice to get answers to. There seem to be an endless stream of nice-to-know things, but we try to focus primarily on issues that are relevant to our business. However, understanding our customers – knowing what should be sold, to whom and when – and enhancing targeting are the key issues.