How to increase sales by mapping buyer behaviours
Gut instinct about what your shoppers really want during their in-store customer journey should no longer be driving your business decisions – instead, data science should be the key driver of retail success in the coming decades.
Today, 56% of all customer interactions happen during a multi-channel, multi-event journey. And 38% of all customer journeys involve more than one channel of interaction. According to McKinsey, a deeper understanding of the customer journey can lead to insights that are 30-40% more predictive of customer satisfaction. Therefore, understanding and optimising the entire journey – not simply individual experiences – can create huge value for retailers.
Traditionally, stores would monitor purchases and buyer behaviours, in order to learn how to improve next time. However, with the emergence of big data analytics, organisations can now go beyond seeing what happened on the journey (the individual experiences) and delve into why and how certain behaviour occurred along the journey. By adopting this approach, retailers can define actions that encourage success and eliminate failure. So, let’s take a look how big data analytics can actually provide retailers with the knowledge to improve the customer journey.
Link multiple experiences into a single journey
The true customer journey is the sum of the different experiences customers have over different channels and touch-points. Each experience can cause behaviour changes that alter the journey in a positive or negative way.
Big data allows you to bring together the entire journey (sometimes over many years and transactions) and dig deep into the data to see what the experience was and the impact on the journey. It’s important to gain visibility of all three of these elements — journey points, experience and impact in order to understand what actions help to create an unrivaled journey.
Uncover Hidden Correlations
With all this data, many of your insights might be well hidden. In the case of the customer journey, you need to find the unique correlations that create the big picture. Leading big data analytics platforms give you advanced analytics and data discovery to find the hidden patterns in the data that make up the journey and more importantly, are guiding the journey. This may lead to correlations and conclusions you never considered before such as:
• Unknown paths customers are taking
• Different time sequences in taking actions
• Sentiment that customers express along their journey
• The behavioural reactions taken based on certain experiences
Delve into the minds of your customers
Behaviour plays a major factor in determining outcomes. Therefore, it’s an essential component of your customer journey analytics. Behaviour analytics shows you the mindset of the customers at each step in the journey and more importantly, what led them to take their next action. The digital age has given us a huge amount of data that help us understand customer behaviour. Big data analytics enables you to harness this data to directly correlate experience to next actions, which is behaviour. From this data, a company can optimise and personalise the experiences to impact behaviour and guide the customer down the journey to right outcomes.
Increase the Lifetime Value of Your Customers
In today’s world, a customer’s journey doesn’t end with one purchase. Your goal is to create a long, profitable lifetime relationship with the customer. After all, it is called Customer Relationship Management. Using big data-driven insights, you can devise advanced strategies to retain customers, and sell add-on or complimentary products. Advanced customer journey mapping means you’ll be able to gain more value from each customer.
Increase Your Ability to Experiment
Big data analytics has the potential to increase the speed at which organisations do business, adapt to change, and find what works and what doesn’t. Some people call this “agile.” But we prefer to call it ‘learning to fail fast.’ Experimenting with a big picture view does not give you the granularity or ability to apply actions to your experiments. But with data-driven customer journey mapping, your experiments and testing will produce more insights, be directly applicable to relevant actions and allow you to see the full impact of your efforts.
By using big data to understand customers and their journeys, organisations can gain deeper insight into customer psychology. They can also uncover hidden correlations that reveal behaviour patterns and the actions they can take to impact the behaviour to produce positive outcomes. This leads to a longer relationship, a higher customer lifetime value and increased customer retention. Organisations that use big data to optimise the customer journey not only create more profitable customer relationships, but also create competitive advantage in highly context consumer markets. Just think how often you purchase from the companies with the most streamlined journey with a superior experience and imagine what that could mean for your company.
If you’re looking to embark on a big data project, ANS’ big data services can help you to build highly scalable and secure Big Data applications at pace which can be combined with high-powered analytics to help you make smarter business decisions. We work with Hyperscale public cloud providers to give you fast access to flexible and agile IT resources, so you can rapidly scale any big data application.
The UK’s largest car part dealer, ECP turned to ANS to gain deeper insights into their business. The organisation had already collated years’ worth of data but this was fragmented and stored across a number of different systems, making it impossible to extract meaningful information. By partnering with ANS, ECP have been able to process this data in a cloud environment that is offering flexibility and scalability to enable them to analyse and compare data to provide real-time updates on stock availability as well as ensure they were purchasing the right products at the right time and for the best price. To read the full case study, click here.