Customers are unpredictable and understanding their buying behaviour is simply not possible. Well, not actually. This statement is only true for companies who have not yet harnessed the power of predictive analytics to gain customer insights. Up until now, most companies tackled customer retention by taking corrective steps after they went wrong to avoid losing out on potential customers. Today, companies can use predictive analytics to anticipate customer spending and focus on creating the right opportunities to foster customer loyalty.
A lot is spoken about analytics, particularly customer analytics, which is not an entirely new phenomenon and has in fact been around for quite time. Back in the 1950s, when businesses began to realise that every customer is different from the next and each have their own needs and wants, they started gathering data about their customers to begin to understand how these needs can be met. This saw the rise of customer segmentation according to demographics, physiological and social metrics, among others.
Fast forward to present time and the picture is similar, however, the amount of customer data now available has grown exponentially and a number of tools and technologies are available to decode and understand it. It is easy for companies to get lost in a deluge of data and have no idea what to do with it. That is the purpose of analytics – to thoroughly interrogate the data and go beyond the data to find the underlying stories that can drive greater business success.
Analytics can be divided into three broad types; descriptive, predictive and prescriptive analytics. In simple terms, descriptive analytics looks at the data and summarises it, predictive analytics analyses data to determine what is likely to happen, and prescriptive analytics looks at what can be done and makes the necessary recommendations for sound decision-making.
Gartner research reports that by 2020, predictive and prescriptive analytics will attract 40% of enterprises’ new investment in business intelligence and analytics.
Painting Accurate Customer Portraits with Predictive Analytics
Predictive analytics can be particularly valuable to a company’s overall customer experience. Through analysing both the structured and unstructured data, this technology can highlight historical patterns in relation to customer behaviour and use this to forecast future behaviour for particular customers on an individual level. In addition to pattern matching, predictive analytics also makes use of various other techniques, such as predictive modelling, forecasting, regression analysis and multivariate statistics.
One of the advantages of predictive analytics is its ability to take data and drill down to a granular level, for example, an individual customer. Given the vast amount of data that a single customer can generate within one day, it is no wonder. When analysing the data and uncovering unique insights on the behaviour of one customer, a business can tailor-make a customer experience to the needs of the individual that has a great chance of delivering the desired outcome. This approach applies to engagements with the customer in respect of resolving a complaint or closing a sale.
By carrying out advanced segmentation strategies through the use of predictive analytics, companies can identify previously undiscovered niches based on consumer behaviour and in so doing enhance the effectiveness of their overall marketing activities. This synchronisation between company and customer has the potential to develop deeper customer relationships and significantly improve customer experience, giving brands a solid competitive advantage that can lead to greater market share.
This technology can highlight historical patterns in relation to customer behaviour and use this to forecast future behaviour for particular customers on an individual level.
A common measure to gauge the efficacy of customer experience initiatives is customer lifetime value. By increasing the customer lifetime value, companies are successfully satisfying their customers’ buying expectations and in so doing they are able to grow client spend. Interestingly, research conducted by Aberdeen Group found that companies adopting predictive analytics into their customer experience management programmes report a 98% greater increase in customer lifetime value, when compared to those who do not.
Data Driving Next Best Actions for Selling in Contact Centres
Contact centres traditionally deal with service-related requests and resolve customer queries, however, offering customers qualified up-sell and cross-sell opportunities as part of their interactions can transform a typical call into one that generates revenue. The key lies in knowing which opportunities would present the most benefit to the customer beforehand. This is where predictive analytics shines. The technology can be used to construct models that analyses buying behaviour data to predict the prime cross-sell and retention opportunities. This information can then be used to assess the best product to meet the specific needs of the customer, or alternatively create a unique offering if the need exists.
To further improve the customer intimacy, customer service agents can capture the information gathered from conversations to direct the next best action based on the contextual relevance for each customer in real-time. During the conversation, customer service agents can also direct the discussion by posing questions to the customer and follow a decision tree based on the customer’s profile to guide the best possible outcome.
Gartner research suggests that by applying simple and well-defined business rules via a decision tree, is merely a short step from the predictions to determining a specific course of action. Predictive analytics has the ability to feed data into prescriptive analytics techniques to help companies determine how the predicted outcome can be influenced by the next best possible action. This approach is not exclusive to the contact centre, but can be applied to a number of different areas of the business.
Partnering with service providers that possess advanced analytics expertise is a recommended approach to kick-start analytics initiatives.
Effectively Improve Customer Retention
Generating new customers can be expensive, which is why retaining the old ones should be a priority for businesses. To acquire a new client a number of expenses need to considered, such as purchasing marketing lists, developing targeted marketing campaigns, deploying sales representatives and paying the necessary commission for converted sales. In comparison, retaining a customer requires a great deal of effort, but is significantly less than acquiring a new one. This effort includes a sound understanding of a customer’s needs and developing an enriching personalised experience founded on the information gathered from analytics techniques.
According to data from the Aberdeen Group, companies that employ predictive analytics retain 27% more of their customers. Findings show that companies without predictive analytics are also more than twice as likely to be challenged by an increasing cost of customer acquisition compared to those using the technology.
Companies without predictive analytics are also more than twice as likely to be challenged by an increasing cost of customer acquisition compared to those using the technology.
Implementing a Company-wide Predictive Analytics Strategy
As with any other strategy, the first step is to identify the objectives and align it with the overall company priorities. To think that predictive analytics can effectively yield the desired outcomes without collaboration is short-sighted. In fact, effective initiatives rely on cross-company collaboration. Thus, it is crucial to involve all the relevant stakeholders from the various lines of business from the outset and win their support.
Involving the necessary stakeholders is critical to determine what the existing tools and resources available are, and how best these can be leveraged. It is no secret that data scientists are in high demand. Instead of hiring from the outside, it would be worthwhile to identify individuals who are already functioning in a similar role internally. However, don’t overlook those without data science degrees, because the advancement of tools and easy-to-use products makes data exploitation a little bit more accessible. Gartner suggests that business analysts can take on the role of citizen data scientists and are well positioned to assist the traditional data scientists to extend the reach of predictive and prescriptive analytics capabilities.
Sharing is caring, and this mindset can significantly help to achieve the end goal, especially when sharing existing proprietary intelligence. Establishing a process that assists in sharing, validating and managing analytics models as well as communicating its applicability for use can help expand the use of data science throughout the organisation and tap a consistent benefit from it.
Partnering with service providers that possess advanced analytics expertise is a recommended approach to kick-start analytics initiatives. Often, these partnerships allow for the transfer of skills and lessons that help to build internal capability and intelligence. It is preferable to partner with a provider that not only has predictive and prescriptive expertise, but also a domain of knowledge specific to a company’s respective industry.
Leveraging predictive analytics presents a number of benefits and has the ability to drive profitable outcomes. Companies can use it to address attrition and reduce its impact on the organisation, resolve complaints quicker with specific improvement initiatives at key touch points and improve the efficacy of cross-selling efforts to generate increased revenue.
In order to tap the most benefit from predictive analytics, open and effective communication is required between all relevant business units that allows for company-wide collaboration to take place. The same collaboration applies to sharing the outcomes from the insights generated to all stakeholders to ensure that the customer experience can be improved continuously. This approach breeds consistency and spills over into the customer experience, which reduces customer effort and builds loyalty.
Customer satisfaction and bottom-line performance is irrefutably linked. No single company can exist without the support of its buyers and therefore retaining existing customer and attracting new ones is vital to company growth. Companies risk losing their customers to competitors when intimacy starts to wane and begin to lose touch with their needs. Predictive analytics has the ability to gain insight into the nuances of customer behaviours to predict possible actions and best meet their needs, deliver brand promises and capture a greater wallet share.