It’s a question that has turned many in our field to the age-old answer: “I work with computers.”
A quick google search of the question doesn’t help either. It only brings more confusion by showing a shopping list of buzz terms from major tech companies.
Once you work in the field for a few years, it’s easy to see where the difficulty in finding a definition arises from. If you ask ten Data Scientists what Data Science is, you’ll get at least 10+ different answers. However, after listening long enough, a similar thread starts popping up.
Stripping it back to basics, let’s look at the makeup of the term:
“Data” – it’s what this whole exercise is for, looking at and analysing data.
“Science” – it’s referring to the application of scientific methods when dealing with data. Any other add-ons are the result of the versatility of Data Scientists.
Therefore, combining all the above into a simple definition of Data Science, we get the following:
Data Science is the incorporation of various techniques when analysing and interpreting data.
In the field of Customer Decisioning, a Data Scientist can support with analytics, modelling and the implementation of AI solutions. In a previous article, (Customer Decisioning: Why it’s Crucial to an Unbound Experience) we touched upon the benefits of treating each customer as a ‘segment of one’ and briefly mentioned how Data Science capabilities can further enhance the process. That’s absolutely the case; the power of the Data Scientist is the value added on top of existing solutions. In this section, we’ll expand on the above-mentioned point.
Normally, there would be a way for a business to establish a next-best-action:
Both points are important tools in a decisioning framework. However, sometimes you’ll want a bespoke add-on - something that requires a broader understanding of your underlying customer data and that is more involved than the out-of-the-box solution.
For example, it could be a propensity model to predict which customers are likely to purchase an item. Perhaps that model needs to be a cog in an existing system and its output will feed into a different model. A simple example for the above case is a messaging prioritisation solution (adaptive model) that takes a propensity to purchase model (custom built predictive model) as a predictor. With that predictor it can decide whether to send a promotional email on said item or present specific discount to a customer to incentivise a purchase.
Typically, a Data Scientist will build a model based on a certain specification and through the model lifecycle, they will perform monitoring and retraining activities. A model can be built using any major language in a widely transferable format locally or remotely on a cloud service (e.g. AWS, Google Cloud). A versatile Data Scientist can use a variety of tools and have quality checking in place to monitor and retrain a model when its performance drops.
It’s that focus on model governance, combined with the passion to see their models drive business activities and influence customer engagement, that make a Data Scientist stand out from their peers.
Another point briefly touched upon was AI. These days, AI feels less like a choice and more like a rite of passage to be considered tech-savvy. Nevertheless, the unknown nature of AI algorithms can pose a threat to data security and needs to be used with proper guidance when implemented, otherwise the output from models may produce pointless results and waste resources.
A Data Scientist can provide a clear picture of which sides of AI can be useful and how they fit with the current infrastructure. Is machine learning necessary when a simpler model can be more cost-effective and transparent? Do we need manual checks on incoming communications or can a Natural language model help sort comms onto the right people and save time. A Data Scientist can guide you through this journey.
Following the section above and with all the praise and patting on the back I’ve given to Data Scientists, job’s done. What else is there to say? It appears Data Science and sliced bread are on the same plane of existence. Happy days, we found the solution to all our analytic woes! It sounds too good to be true, doesn’t it?
Of course, the reality is a bit more complicated, so let’s not dethrone sliced bread just yet. If Data Science is that amazing, why isn’t it more prevalent when making decisions? We touched upon some of the reasons above.
Here are a few to consider:
There are valid reasons to be cautious when approaching modelling and AI. We’ve all seen the headlines with data safety and transparency being on the top of the list for many clients. We understand that it’s hard to get out of established practices, especially when technology and guidelines are changing at a never-before-seen pace. We understand that a reliable partner is required on this journey to build trust and remove the complexity and fluff that’s currently surrounding Decisioning and Data Science.
In summary, brands are leaving commercial value and business benefit on the table by not utilising more Data Science capabilities within their customer decisioning platform. Data Science techniques are a key component when it comes to maximising the power of the single customer decisioning brain. Brands need to take advantage of modern advanced analytical capabilities to drive the very best omni-channel customer experience and create a long-term competitive advantage over their competitors.
At Merkle, we understand decisioning and Data Science discussions can be difficult. Combining both can be a further complication but, at Merkle, we’re here to provide support and guidance along the way. Our Customer Decisioning practice has experience delivering precise and personalised interactions that focus on establishing customer-centric excellence. Our Data Science team is equipped with the knowledge on modelling techniques, advance analytics and AI solutions that aim to deliver timely and actionable insights.
With Merkle's help, businesses can deliver exceptional customer engagement in this competitive landscape, fostering enduring relationships. If you’re considering a complete transformation of your customer experience, get in touch with Merkle today to drive real, valuable results.
Want to read more? If you missed our last Decisioning Congress, you can access all the valuable content on-demand from across the two days. Explore the five stages of the Decisioning Life Cycle and gain actionable insights to become a world-class decisioning business.