Real-time and Predictive Analytics

Stop what you’re doing and grab someone in marketing! This month’s theme of predictive analytics is probably one of the most important topics for discussions around how customer experience increasingly fits into a wider picture of Sales and Marketing. 

One of the most rewarding parts of my job, because it makes me seem really smart is explaining to C-Level stakeholders within large companies the business benefits a well-run contact centre delivers. 

It's surprisingly simple:

• A well-run contact centre engenders loyalty within a company’s existing customer base.
• Loyal customers will buy more, increasing upsell and cross-sell opportunities.
• Loyal customers become brand ambassadors attracting new customers.

Increasingly the effectiveness of customer service is built upon data. What data you have access to, how it's presented to the customer and how the agent utilises this data. Access to this data and its analysis is getting closer and closer to real-time and is increasingly being used to make decisions in near real-time as well as predict future behaviours.

However, for too many people a contact centre is a business within a business, disconnected from the other. Unfortunately, this is often a “class” differential. This view creates a barrier to utilising the rich data sets generated by customer service for the wider benefit of the whole business.  

The contact centre is the focal point for data within businesses where customers are linked to a business’s process. Every call, email, webchat, SMS and social media interaction is generating data. Consider that the US Library of Congress’s 39 million books and 73 million manuscripts equals around 15 TB of data, IBM estimate some 2.5 quintillion bytes of data are produced every single day.

The evolution of contact centre and why Real-Time Analytics Matter

I like to see the contact centre in its evolutionary context, tracking where it's been and where it's going. This can be split into 3 distinct phases with the presence of real-time and predictive analytics becoming more and more important at each stage.

The Past: Call Centre

Call centres of the 1990s and early 2000s. These too often focused on low-skilled, low paid agents to provide basic inbound customer service for voice and outbound dialling operations. These only operated 09:00-17:00 and only used voice. Many of these were outsourced offshore, much to the detriment of customer satisfaction. Access to real-time information was limited to basic wallboards which provided limited analysis of the real-time situation.

The Present: Contact Centre

The situation for most businesses at present. Advances in technology and customer demand brought many customer service operations back to the UK. Most contact centres are now using multiple channels, however, each channel usually has its own system for example;

On top of this agents will be storing information in a CRM system. This means the business must use many different systems, increasing costs and lowering productivity. Access to real-time information is there but aggregating it together to be useful is difficult.

Workforce Management can take information from voice ACD’s and some other channels into account when analysing, forecasting shifts and reporting on real-time adherence.

The Future: Customer Engagement Hub 

The Customer Engagement Hub can be achieved when enterprise CCaaS and CRM technology is deployed to their full potential. It converges all channels into a single system and a single workspace for agents to deal with customer contacts. Conceptually it also converges contact centre infrastructure (CCI) and customer relationship management (CRM) systems into a single platform to handle all customer engagement. This means the integrity of the data we can gather about a customer is greatly enhanced and thus can be used for proactive Sales and Marketing purposes.

Propensity Modelling

The ultimate goal for real-time and predictive analytics is to use the data generated by the contact centre for propensity modelling in a similar way to how marketing utilises interactions with a website to identify new leads and prospective clients.

Propensity modelling attempts to predict the likelihood that visitors, leads, and customers will perform certain actions based on their behaviours interacting with a firm. It's a statistical approach that accounts for all the independent and confounding variables that affect said behaviour.

For example, if you’re a retailer and someone web chatted you or emailed you to discuss a current product, can we use that information to enhance a Sales or CX outcome:

• Can we use that information to up/cross-sell other products e.g. extended warranty or complimentary product?
• Use the interaction to proactively contact customers about an issue before it causes a spike in interactions.

However, for this to work we need deep integration between customer experience and marketing solutions. The results for early adopters though will be a distinct first-mover advantage over their competitors as well as an increase in sales, retention and C-SAT.

The Precedent for this is Workforce Engagement Management

One of my favourite terms for customer service advisors or agents is “Internal Customers” and this is important when thinking about the ‘Customer Engagement Hub’. WEM technologies, especially workforce management provides an established precedent for how we can use real-time and historical data to predict demand and from which we gain better insights and enhanced efficiencies.

The principals behind WEM when analysed in the round can equally be applied to external customers as well as internal customers:

• Collecting and consolidating detailed data sets
• Analysing those data sets
• Advising an outcome from those data sets
• Monitoring the real-time performance against predictions

Conclusion

Real-time and predictive analytics are core to the future development of customer experience in the 2020s and is a core driving force behind the direction of the customer engagement hub.

The ability to use real-time and predictive analytics to bring customer service under a wider banner of sales and marketing strategy is a big change that will disrupt how the wider business view the contact centre.

However, there is a huge opportunity within this change. It will support investment in new more interconnected systems and roll back the segregation of contact centres and their staff from being other to being a core and strategic focus point within the business.