When marketing teams are then trying to give increasingly personalised customer journeys across multiple touchpoints, siloed data (and systems) can prove a real challenge.
One of the biggest challenges we see with clients when they are (re-)embarking on their data journey is data siloes. Often, they are there for a purpose but sometimes data siloes exist due to legacy architecture decisions or the lack of a business-wide data strategy.
To overcome this, the customer data platform (in various guises from a re-badged Email Service Provider through to a full functionality CDP) has grown in popularity in recent years. This solves a lot of challenges across identity resolution, compliance, activation across multiple touchpoints and centralising customer data sources. But many of these platforms also offer machine learning capabilities – usually the domain of data scientists and analysts – that are out of the box and easy to implement.
There are good reasons why these platforms have developed these capabilities outside trying to build a competitive edge – given the shortage of data scientists in the market it is inevitable that technology will try to compensate, and often do so pretty well. While much of the focus to date has been on solutions that align to the retail sector, this will change over time as more ML/AI solutions become available.
Working in the analytics and data science industry, it is tempting to dismiss this (still developing) capability in CDPs – how can a generic model beat a bespoke solution with additional data sources and business knowledge? The obvious answer is that it can’t – but that misses the point. The shortage of data scientists and the need for speed to delivery gives these out the box solutions an edge and even if they are ‘sub-optimal’, they are almost certainly better than doing nothing.
So where does that leave data scientists who operate in the customer space? CDPs are stepping into the more operational marketing and customer solutions but there are always new techniques, new data sources (many of which don’t naturally fit into the CDP ‘data model’) and new business problems to solve.
One thing that we have observed at UNLIMITED over the last few years is that many of our data science projects are either directed at very specific business challenges, involve the integration of new data sources or are a level above the operational marketing challenges and more strategic in nature.
In essence, data science is being elevated to solve bigger, newer and more complex problems – which is actually a pretty good place to be.
At Realise we have helped many businesses understand their needs and navigate change. Contact us to find out how we can help you find the right solutions for your business.