Data may be expanding exponentially, but this expansion in itself is not the be-all and end-all. Data is very important, but only because it enables organisations to learn more about their customers and offer them a better service. Therefore – and this is crucial – data allows organisations to make much better decisions. But can the data platform companies are currently using facilitate the right decisions? A recent discussion opened my eyes to some real issues about data use and where some of the challenges lie. Here are my top six takeaways from the discussion.
1. Poor data usage is a very big deal indeed
Companies that cannot make good use of their customer data are already at a disadvantage. Data from Gartner shows that fewer than 10 percent of companies have a holistic view of their customers. Worse, only 5 percent use that view to support business growth. That means that most businesses are not capitalising on the data that they probably already hold to understand more about their customers. They are unable to tailor relevant offers and marketing that fits their needs and are therefore likely to be missing out on sales.
2. A genuine all-around view of the customer journey needs to draw on a wide range of data
A 360-degree view of your customers means having a full understanding of their activity and history of engagement with the company. It therefore needs to include and draw on data about all their digital activity across all channels, including websites, mobile apps, social media and any connected devices. It must also cover transactions, purchases and use, historical activity, and contact history. The issue, however, is that much customer data is held in silos, and in many companies, it is not brought together. There are also questions about data quality and governance like the reliability of new sources of data. This can only really be overcome by working within a strong data governance framework to provide quality assurance.
3. Data usage is, at its heart, about scale
Millions of operational decisions are made across every organisation every day. Organisations need the ability to make effective and profitable operational decisions quickly, based on ever-increasing volumes of data. They therefore need powerful and accurate analytical models and platforms that enable them to sift through the data and use the right information at the right time, whether that is in real time or not. They also need to be able to incorporate new data as and when it is generated. All the time – as customers interact with the brand both online and offline.
Based on this data, companies can orchestrate the journey of their customers and engagement. It is all about offering the right services or products to the right customers, at the right time, via the right channel. As long as the relevance is there for the customers – of course taking into account the regulations around data privacy – the results can be impressive.
4. If scale is important, however, trust in the data is also essential
I mentioned data quality and data governance before, but it is so important that it is worth mentioning again. You have to be able to trust your data. Regulations on data protection are increasingly stringent, and some have suggested that this may kill innovation. I think the reverse is likely to be true: Compliance is likely to mean that companies have a much better understanding of what data is available to them and what they already hold. It should also improve data quality through better governance. This will give companies a strong foundation to make reliable and trusted decisions, and therefore support data-led innovation.
5. Despite progress to date, the challenge ahead remains large
It is clear that there are huge challenges to simply getting a holistic view of customers. For example, moving to real time can be a massive issue. Many real-time interaction tactics fail because key ingredients for success are missing. These include behavioral data, predictive analytics and a single view of the customer. Data can be used to improve personalisation, and integrated systems can allow for omnichannel interactions. Companies need to think coherently about what they are doing and make sure that they have all the necessary systems and data in place.
6. AI will supercharge human capabilities to provide a better customer experience
Advanced analytics, and particularly AI, is likely to create more insights in very different ways. Possibly more importantly for many organisations, AI can provide cost savings and efficiencies by enabling people to use resources differently, and particularly to help them focus more clearly on customer experience. AI is not about replacing people but adding to their capabilities to provide a better customer experience. Chatbots, for example, can speed up telephone or online customer service response times, with people stepping in rapidly when the conversation gets tricky.
The longer view
Consequently, yes, data can improve the decision-making process about the customer. But it’s certainly not an automatic process. There is a need to include the considerations described above. More data is only useful when it’s good data. Finally, the good news is that today technology and practitioners are moving in the right direction, making the customer experience more and more exciting.
To learn more, check out this Harvard Business Review report: “Real-Time Analytics: The Key to Unlocking Customer Insights & Driving the Customer Experience.”
Discuss decision matters with experts
Interested in exploring more about intelligent decisions? Join the #saschat on Friday 8th Feb, kicking off at 15hrs CET, 14hrs UK, 9hrs ET, where we will be using these questions to move the discussion along:
Q1 - What are the most impressive machine learning use cases you have seen in production?
Q2 - Who would you consider stakeholders for the lab-to-operations AI path?
Q3 - What are the most common reasons AI fails to make the way from labs to operations?
Q4 - What does it take to orchestrate the analytics lifecycle across business, data science and IT?
Q5 - How do mature analytics cultures balance between choice and control?
Q6 - Which industry segments find it easier to translate AI from testbed to operations, and why?