A key question for businesses is how to make use of this moment, to turn that feeling of goodwill into increased customer engagement or sales. The challenge rests on two main issues:
- how to distinguish between the satisfied customer who would be open to buying more products / services, and the satisfied customer who wouldn’t be open
- how to follow up within a short timeframe while the “window of goodwill” is open.
USING ADVANCED ANALYTICS TO FIND THE SWEET SPOT
At Comdata, we have developed an innovative tool to tackle both those issues. Our new Comdata Polaris lead generation tool uses advanced analytics and AI to predict which inbound care calls present real-time opportunities for cross-selling. It offers businesses a high level of confidence they can hit the “sweet spot” for making outbound sales calls to satisfied customers.
We can then provide timely outbound sales execution, confident there’s the highest probability we’re calling customers who will convert. Given that retaining or cross-selling to existing customers is far more cost-effective than acquiring new ones, this is a powerful tool.
SORTING THE KEY 2% FROM THE 98%
In the International Data Analytics team, we specialize in taking large volumes of data from multiple sources and developing practical business solutions. That’s what we’ve done with Comdata Polaris, developing, training and testing the model with a global energy provider.
Our thinking is that resolving a call to a customer helpline or general enquiries line potentially creates a “hot lead”, with some of those customers – let’s say around 2% – likely to respond positively to a sales call. But how do you sift the 2% from the other 98%?
Using our analytics and AI expertise, we can predict the 2% to a high degree of accuracy. That means more sales opportunities captured, and fewer sales calls wasted to the other 98%. It’s a double win for our clients.
SECTORS FROM UTILITIES TO MEDIA TO FINANCIAL SERVICES
We see applications for Comdata Polaris in a wide mix of sectors beyond energy and utilities, including media, telecoms, banking and insurance, and other sectors where customers who call with general queries could be prospects for selling additional products or services.
In the government sector too, Comdata Polaris could predict which callers might be receptive to participating in a specific programme. This could be anything from signing up to a citizens’ volunteering initiative to registering for vaccinations.
We are also looking at its application in other interaction channels, beyond inbound and outbound phone services.
THE METHOD BEHIND THE MODEL
The model behind Comdata Polaris is based on a data integration approach. It looks at a complex mix of client, socioeconomic and our own operational data, putting customers’ likely behaviour at the heart of the analysis. The goal is to pinpoint which callers might be open to an outbound sales call for specific services or products, and when would be the best time to call them.
To develop the tool, we initially looked at over 300 variables for each client, using a rich mix of data:
- Comdata operations data – such as information on shift patterns and the timing of calls
- Client data – such as CRM, credit history, usage data on existing services
- External government data, such as household income and spending
- Indicators created through “feature engineering” of other data, for example a customer’s energy consumption relative to others in the same postal area, or their loyalty score based on renewal history.
Through training and testing the model, the initial 300-plus variables were reduced to 59 key predictive variables, most of which are engineered from different data sources. The model used for each region, sector and customer applies different weightings to these variables to get the best fit.
USING COMPLEX DATA TO DELIVER SIMPLICITY
The guiding power of Comdata Polaris comes from this unique and highly engineered combination of socioeconomic, customer and operational data, which we use to cut out the noise of businesses’ customer interactions so they can focus on the best leads.
This achieves a level of prediction and accuracy which businesses are unlikely to achieve themselves, using their CRM data alone. In order to detect the “window of goodwill” opened after a positive inbound call, companies must look beyond their static customer data and also use insights about the interaction itself. This requires the highest level of AI and analytics capabilities.
On top of the analytical expertise we offer them, it’s about operational understanding and capabilities – the fact our specialist outbound sales agents can immediately get onto the case. It’s all about using the moment, to make a difficult task simpler for our clients.
The system also orders the prospect list according to the likelihood of conversion, so agents can contact the leads with the highest potential. This allows our clients to decide the cut-off point for calling people, looking at the balance of prediction accuracy and volume of sales they want to achieve.
MORE OPPORTUNITIES CAPTURED AND FEWER CALLS WASTED
The benefits of the Comdata Polaris approach really become clear when businesses look at their existing practices on using the goodwill window opened up when a customer query is resolved.
The first common scenario with businesses that they don’t follow up at all. And that means an opportunity is wasted.
The second typical scenario is that they may call people back but their approach is scattergun or too generalised. They don’t have enough data or analytics so they call people who are unlikely to buy further services, or they call at the wrong time of day, or when the lead is no longer hot. That investment in sales activity is misdirected.
A third scenario is that businesses want to use inbound calls as a sales opportunity and encourage agents to use “next best alternative” and other approaches during the service call itself. This is rarely cost-effective as even the best frontline care agents are rarely expert salespeople. That means businesses end up extending call times and costs, without the reward of higher sales.
A fourth scenario is that businesses antagonise customers through an unwanted follow-up sales call. That’s possibly even more serious than the first scenarios, as it’s a customer relationship potentially damaged.
With Comdata Polaris, businesses can not only avoid these risks, they can also focus their outbound sales activity on fertile ground.
Using the tool, businesses can invest in follow-up outbound sales calls to customers who feel good about the company, have a good credit history, and are likely to have an appetite for the product/service and a lifestyle to suit it. And the speed of the tool allows this to be done before the customer’s feel-good glow fades and the sales window closes.
Comdata Polaris separates the data-driven identification of the right lead from the follow-up sales activity. Each party does what they do best – the care agent focuses on the customer experience, the machine identifies the best prospect, and the outbound sales team converts the sale.
LINKING TO THE WIDER UNIVERSE OF CHANGE
Transformational in terms of leveraging incoming calls to boost sales, Comdata Polaris also links to broader patterns of transformation in business.
The consultancy McKinsey & Company recently highlighted the case for companies to use advanced analytics and machine learning to “turbocharge their forecasting capabilities”. It gave the example of sales forecasting and demand planning, where the more accurate and timely forecasting made possible by analytics and AI can inform allocation of resources and build trust.
With Comdata Polaris, we are right on the McKinsey mark, using advanced analytics so that our clients can allocate Customer Management resources more effectively. This in turn allows their own customer management teams to build trust internally – generating a track record of accuracy in outbound sales campaigns and investment that can be useful in future budget rounds.
Comdata Polaris also addresses another issue raised by McKinsey’s: the skills gap in data science and AI in many traditional organisations, and the delays which this causes. Through applying our own expertise to an outsourced option tailored to each business, we are filling that gap too. It’s all designed for our clients to get tomorrow’s solutions today.