There is a long-standing debate about whether decision making should be based on art or science. In fact, in 2010 a Harvard Business School blog took an in-depth look at just this. Whether it is art or science that helps provide the answers, the most important part is that the decision making doesn’t impact the customer journey. A lot has changed in the last ten years, and while digital transformation and the increased expectations for faster-paced decision was already on the up, the pandemic has moved it into warp speed over the past year.
Consumer expectations of businesses are high and on the rise. In fact, according to recent research from Experian, 60 percent of consumers now have increasing expectations for their digital experience than before Covid-19. The same research also found that 1 out of 3 consumers are only willing to wait up to 30 seconds before abandoning an online transaction, especially when accessing their bank accounts.
Since the pandemic hit the global scene in March 2020, businesses have moved quickly to accommodate the massive shift to digital channels by rapidly adopting new technology. Digital transformation actually rests on two pillars – automation and insights. In order to combine these two pillars of digital transformation and keep up with consumer demand, businesses need to be able to quickly gather the right data and analytics and then apply them in real time to provide positive experiences. By bringing together the relevant data and predictive power of emerging analytics techniques, businesses – big and small – can meet these heightened customer expectations to make decisions quickly, easily, fairly and safely.
While traditionally decision-making has helped with customer prospecting and acquisition, truth is that modern decisioning solutions can help drive growth and stronger engagement through the entire customer lifecycle:
Businesses need to be able to support consumers by establishing who they are, what they need and how and when they want to transact. there are a lot of pieces to get right, but with the right data and technology it is possible. In order to better reach customers and optimize decisions, businesses need to be able to predict consumer behaviors and patterns such as understanding when a certain customer typically makes purchases. Businesses can also leverage data and advanced analytics to profile their existing customers and recognize matching prospects, opening up a new pool of potential customers. Businesses can also access data and analytics to help identity and target new customer segments that haven’t historically been served.
Acquisition & On-boarding
To compete in today’s market, organizations need to drive more intelligent customer acquisition processes to create profitable, sustainable relationships. Imagine knowing if a prospect was someone that has the potential to be a VIP user as opposed to somebody that browses but won’t spend regularly.
Businesses not only need access to this kind of data in advance but they also need to be able to find ways to use the data to deliver more intelligent actions. One way to achieve just that is by taking client data with customer history and third-party data and applying advanced analytics techniques to use the data to help improve performance and profitability.
And let’s not forget about the most important part of the lifecycle – your customers. Making sure that your customers are at the heart of every decision is critical. In order to provide the experience that today’s consumers expect businesses must be able to track and analyze customer behavior and decisions over time to improve their business models, known as decision logic.
While we still aren’t able to achieve scale if we individualize our outreach for each single customer, we are able to store customer preferences and actions and build that data into models that provide a personalized experience. In fact, according to McKinsey, companies who had carefully created omnichannel strategies to create unique, compelling customer experiences have had to throw out their playbooks and improvise to keep pace with the explosion of digital transformation.
Today’s unprecedented marketplace has only highlighted the challenge that exists when businesses try to avoid risk. The processes often fail to detect signs of trouble because decisions are made at department levels. Knowing your customers is your greatest defense against risk and if their circumstances change rapidly, which many have in the last year, you need to be able to quickly adopt to those changes. In fact, by being able to fully understand potential risks you can use the information to act on the insight and make the right offers for the right customers.
Identity and Fraud
One of the hardest challenges businesses face today is finding the right balance between keeping a customer both satisfied and safe with their experience. Detecting fraudsters from legitimate customers is still challenging and unfortunately ends up with businesses leaving money on the table. This is another opportunity for business leaders to apply machine learning models to the vast amounts of data they have access to in order to make sense out of that data and better control their risk and exposure.
Today more than ever the pace is fast and actions are decisive – in fact, a report from Microsoft on the global state of customer service indicated that 61% of customers have ceased to do business with a brand due to poor customer service. Consumers are quickly shifting their behaviors and expecting a lot from the businesses they interact with.
Regardless of where they conduct their business, consumers expect a secure, convenient experience and they will quickly abandon a transaction if they experience friction in the process. The businesses that will excel in this market are the ones that master the science by finding a way to automate decisions using technology and data and master the art by applying a layer of human oversight to ensure the right decisions are being made. Most importantly, they will use science and art to deliver outstanding customer experiences.
(The author is Executive Vice President of Global Decisioning at Experian and the views expressed in the article are her own)