Omnichannel Data is the key to a Successful Omnichannel Strategy

Implementing a successful contact centre omnichannel strategy requires the coordination of many aspects of your organisation. A strong marketing technology stack and omnichannel platform, well designed intelligent routing and automation, and intuitive agent desktop and knowledgebases are all key to omnichannel success. However, one aspect that is often overlooked when operationalising an omnichannel approach is to first ensure that your omnichannel strategy itself is optimised for success.  Although many organisations talk about channel-spanning omnichannel customer experiences, few actually execute their vision in a coherent way. And even fewer deserve the distinction of being called “omnichannel.” Indeed, most companies that talk about omnichannel CX continue to focus on optimising the customer experience within individual channels, rather than across them.

So how can your company break out of the siloes and deliver true omnichannel customer experiences across the entire customer journey? Designing a successful omnichannel strategy starts by asking the right questions. What are the common cross channel friction points? What customer expectations and preferences does your company fail to meet? What frustrations and pain points is your CX accidently creating? What blind spots in the customer journey do you have, and which are most important to fix? 

The answer to these and many other pertinent questions can be found in the data that is already flowing into your call centre or contact centre.  In this article, we talk about giving your omnichannel strategy a data twist, and show the importance of utilising customer data to help shape and optimise your omnichannel approach.

The evolution of omnichannel strategy and customer journeys

We see omnichannel evolution occurring in three distinct stages. The first iteration, multiple-channel, occurs where two or more customer interaction channels exist, but they are not integrated. Data exists in their own silos, and it is difficult to access and act on data that lives in different departments. You know you’re a multiple channel organisation if customers mention things like “you don’t remember me between visits” or “I can buy online, but I can’t exchange / return an item in-store.”

Next comes multichannel (or multi channel), where customer support channels are integrated at the data and process layer (e.g., in an electronic back office). Data about the customer is preserved and exchanged between transactions when the customer switches channels (even in mid-transaction). Diagnostically, the customer feels remembered and more valued. But, each channel looks and feels differently and is built around the underlying business process, rather than customer needs and consistency. In this model,  data is more readily available, but it is still challenging to build a comprehensive picture of the customer that can be acted on holistically.

True omnichannel experiences happen when the multichannel capabilities of the organisation are designed and integrated from the outside in to provide a seamless, coherent customer-centric experience; where customers use each channel when and where it suits them and can move among them with ease. This rarely happens if there is not a person or team within the organisation who is tasked with optimising customer experience or journeys. 

A key enabler for multichannel and omnichannel environments is the data. Here is where the experience of working with Big Data can pay dividends. Omnichannel data will obviously come from a variety of sources and channels, and will need to be integrated and connected to form a single customer view. Of vital importance is to recognise that whilst unstructured data (text, voice, etc.) can offer real insights into customers’ feelings and motivations, it is less readily analysed. Traditional data mining techniques need more structure, so they tend to ignore the valuable insight hidden in unstructured data. The best solutions for data management in this case are usually a hybrid of structured and unstructured databases.

Omnichannel data provides better insights into the entire customer journey across all touchpoints

What does a successful omnichannel implementation look like? How can success be measured? When it comes to data collection and analytics, it is easy to address the directly observable parts of customer engagement across channels (the actions customers take; who, what, where, when, how, how much, etc.). However, the “why” behind an action is much more intriguing and difficult to measure. It’s like cosmological dark matter—we know it’s there, but it’s not possible to directly observe it. Of course, we can use a combination of quantitative and qualitative studies to get at the causation behind the correlation, but there is a natural limit to how many times we can ask customers to rank their experience or give us a reason for why they did what they did. 

The good news is that in the era of Big Data and the Internet of Things, we now have the opportunity to continuously measure what customers are doing naturally within the channels they use to interact with us. The products themselves, as well as the channels used to interact with products and services, are a wealth of real time data and information. We can then use customer lifetime journey insight and outcomes in our analysis, making more informed decisions across the organisation and its channels.

Omnichannel success starts with understanding your customers and your company

In apply analytical techniques to omnichannel design and diagnosis, we are interested in two main dimensions: the things customers value from an interaction, and the things that a company does. From there, a simple model provides some very high-level indicators about where to focus omnichannel attention (see Figure 1).

What we do: It is straightforward to identify activities already underway, and use a mixture of qualitative and quantitative techniques to measure them. If we are already doing something that customers like, then keep doing it (exploit). If we are doing something that they don’t value, we then have to assess why we are doing it (for example, to meet regulatory need). When possible, we should either stop doing it (exit) or explain why it is necessary. This is the domain where analytics around observable behavior can reveal those occasions where you are theoretically delivering what the customer wants and measuring the actual impact.

What we don’t do: This assessment gets more complicated because there isn’t historical data to analyse. To use a phrase coined by Robert A. Heinlein, we need to “grok” customers—understand them thoroughly and intuitively—to learn how they use channels, and why. This means really getting to understand customers from their point of view. For example, qualitative studies such as surveys, focus groups, detailed interviews, mock-ups or prototypes, etc. can pay dividends. These studies can be analyzed with behavioral sciences like psychology to start to unlock the ‘why’ behind the ‘what’ and form workable hypotheses. In this way, we can identify new interaction opportunities and determine the activities to avoid (evade) or begin (explore). 

Also, as we record and extrapolate customer needs, wants, and values, we not only start to imagine the use cases, but also the opportunities where we can subtlety influence behavior by providing a choice and measure by recording the result. For example, by tailoring when a choice is required, how to frame the options available and present it, we can guide the user toward an optimal outcome. In addition, we can identify and evaluate the intersections with our procedural needs. As we roll out our product and services we can evaluate differentiated responses with, for example, A/B tests.

Conclusion: Omnichannel customer data insights are a key omnichannel strategy

We’ve found that there are two main reasons so many businesses are interested in omnichannel. First, there is the need to integrate business around a central platform that supports improvements in efficiency and effectiveness. This indirectly influences the second reason—truly omnichannel businesses typically offer better customer service and customer experiences that in-turn improves the yield of sales and marketing efforts and results in more revenue. 

As a result, true omnichannel initiatives are focused on the customer experience along the whole journey, not just on technical or process integration or a single moment in time. They should be undertaken with specific business outcomes in mind, such as increasing revenues or boosting retention, while being fully cognizant of the customer at the centre. The transition to omnichannel should not be seen as a marketing-led or IT-led initiative. It should be a strategic transformation which, when properly executed, results in significant and long-lasting return on investment and happy customers.

Be careful of falling into the trap of polarised thinking about customer experience as being about either the mechanics (providing consistent, frictionless journeys) or the subjective world of emotions (how the customer feels about the journey). In the real world, we need some of both. No one is either wholly logical or wholly emotional, not even in business. We can’t force customers to feel or act in a particular way, or ignore the fact that they will have feelings and pre-existing biases. What we can do is create opportunities to be there for them,  and with them, to deliver a mutually valuable experience that excites and delights.