The Three Customer Intelligence Barriers Holding Brands Back
It’s the companies that deliver great experiences time and time again that see customers consistently coming back for more. But amazing experiences don’t happen easily, the driving force behind this is underpinned by a culture of data, where insights provide invaluable customer intelligence. Robust intelligence, powered by analytics helps brands understand their customer and answers the who, what, where, why and how, to make digital experiences great.
However, a big number of brands are getting frustrated in their attempts to build a solid analytics framework, and are learning the hard way that good analytics requires a little more work than simply running a report. That’s not to say that powerful analytics is out of reach for any brand – many common frustrations have simple solutions! Speaking with a number of peers across the industry, these are the three most common challenges that I come across, as well as my tips for overcoming them.
- Lack of data consistency
The ultimate goal of analytics is to deepen a brand’s understanding of their customers. However, this requires a 360 degree view of any one customer’s interactions with the brand, which can be hard to obtain in today’s fragmented data environment. As audiences move across channels and devices, it becomes difficult to track their activity in a consistent movement, with each platform tracing their movements in different ways. In the case of “walled garden” environments, a closed environment where access to data is controlled by the provider, audiences can drop off the radar all together, depending on the data that these platforms chose to make available.
Brands wishing to gain the coveted 360 degree view of the customer need to be able to correlate and standardise their user data across channels and touchpoints. This is where an investment in a dedicated analytics solutions pays off and shows the difference in value to a free tool. Telecommunications company Swisscom competes on experience rather than price. By using Adobe Analytics, the business has been able to create a unified picture of customers as a starting point for building these interactions. Swisscom uses the platform to further refine its communication through sophisticated A/B testing, and even draws from its Machine Learning component in Adobe´s artificial intelligence framework Adobe Sensei to predict what sort of content and experiences will resonate most with any given customer. As a result of this more strategic approach, Swisscom has achieved a 40% increase in its campaign response rate.
- Organisational silos
Fragmentation outside a company is one thing, but fragmentation within a company can be just as frustrating. And unfortunately, it is just as frequent. Analytics has evolved from an activity that was once the sole domain of digital marketers, to an undertaking that is now relevant – if not crucial – to the business as a whole. However, for many companies, internal structures have not yet caught up.
In an ideal world, all departments should be using insights to power better decision making, and a standardised pool of data should be centrally available across the company. And yet the reality is often quite different, with data typically gathered in siloed, or else concentrated in one particular area of the business.
This is not always the case, of course, and some companies are leading the way with their data-driven culture. Just look at electronics company RS Components. Also an Adobe Analytics customer, RS Components shares insights from the platform with the wider workforce, using innovative tools like Slack. The company also encourages individual data maturity through a certification program whose levels are – in an effort to make the process more engaging – inspired by different astronauts. This program is driven throughout the entire organisation, not only marketing, which further underlines the company’s data-driven spirit.
- Too much data, not enough resources
Companies see the huge potential of data, which is why they are scrambling to collect it in the highest volumes possible. But, the challenge remains – how to make use of all of it?
This is where Artificial Intelligence (AI) and Machine Learning can help. Sensei can save time and effort by automatically flagging anomalies, or comparing segments to instantly highlight relevant differences. This functionality helps brands get the most out of their data without needing to hire a whole team of data scientists. For example, Sky UK uses Adobe Analytics and Adobe Audience Manger to gain a deeper understanding of its customers by monitoring and bringing together real-time customer data across channels. The business is then able to use Adobe Target, alongside Sensei to analyse its tremendous volume of customer information to discover the recommendations, services and experiences that will best resonate with each customer, at scale. Through this process the business is able to draw a direct line from customer intelligence to personalized experiences.
Investing in an analytics solution can also help companies make better use of their data by integrating with other marketing tools, like campaign management solutions or content management systems, allowing insights to be automatically reflected in adjustments to campaigns and experiences.
Customer intelligence has the power to completely transform a business, and a future-focused analytics strategy is essential to compete in today’s experience-led environment. Brands know they need to get analytics right – it’s simply a matter of laying the right foundations for customer intelligence success. The good news is, with the right culture and little help from technology, brands will be well on their way to unlocking the full potential of customer analytics.
If you want to find out more you can listen to my full presentation from the Adobe Experience Festival here.