The data behind our customers – from purchase information, preferences, opinions, and so forth – is the key to decoding the customer journey and predicting its future. To get the right data and to understand it, we first need to put ourselves in the client’s shoes.
Finding key data sources and knowing what to do with them
A great example of a source of highly valuable data is to take a detailed look at customer shopping carts; what’s inside them, and what do the contents say about the customer? It’s possible to dive deeply into this data, but as a starting point, we can use it to answer questions that have to do with where the purchase was made, how far from home the customer was when they made it, the use of promotions, the time of day, the exact location of the product on the rack, etc.
Shopping cart information – and any other key customer data source – can be the source of what I like to call Golden Insights. Why? Because so much can be discovered about where the customer is situated in the customer journey cycle through this data. In knowing the customer’s exact placement, companies can much more easily identify customers’ needs and create a tailor-made action plan that optimises customer awareness, satisfaction and retention.
Recap: the customer journey cycle
Let’s have a look at the typical customer journey as a refresher. It generally consists of 8 stages:
- Awareness: the beginning of the cycle, when we need to pay attention to customer needs to deliver the right messages.
- Knowledge (push to pull): the potential customer gathers information; customers today can collect vast amounts of information about your organisation, its products and its services without you knowing about it.
- Consideration (peer opinion): potential customers select which options seem to meet their needs the best, weighing each one against a hierarchy of needs. The potential customer might not find any of the options satisfactory, in which case they return to previous stages or leave the journey.
- Selection (rational and emotional): this stage can be complicated, time-consuming, and may not even result in a deal. For example, buying a house is a multi-stage process that, even after a selection, can collapse due to bad inspection reports, uncooperative sellers or lack of financing options. Buying a can of soup is, however, a simple act of selection.
- Buying: at this touchpoint, the potential customer becomes an actual customer. It’s the perfect time for cross- and up-selling.
- Satisfaction: after a purchase, the customer can be satisfied or dissatisfied with a product or service. This stage isn’t so much about the performance of the product, but about how well it compares to the customer’s expectations.
- Loyalty: the customer becomes resistant to competing options, marketing messages or to returning to the Consideration stage. The cost of generating additional purchases from a current customer is usually much less than the cost of creating a new customer.
- Advocacy: the customer isn’t just loyal to your organisation or product, but promotes it to others.
At every touchpoint on this journey, it’s important to examine the past to understand and predict the customer’s next steps. However, it’s not always the case that the companies are able to benefit from Golden Insights – in fact, it’s less common than you’d think. In 2016, most organisations (99%) use data – albeit limited – to understand what their customers are doing and thinking. 25% go a bit further, engaging in some diagnostics and searching for explanations in data changes. Only 15% dare to run predictive analyses on their data.
Prescriptive analysis: going where few companies have gone before
Finally, only 1 – 5% of organisations venture into prescriptive analysis. It doesn’t simply anticipate what will happen, when and why; prescriptive analysis also suggests potential ways to benefit from a future opportunity or mitigate a future risk. It even indicates the implications of each decision option.
At boobook, we help organisations take this final leap to prescriptive analysis. One of our major success stories is in the financial insurance sector. By collecting information from different sources such as newsletters, transactions, branch distance, website usage, CRM, market research and more, we created a model that allows the company to predict which customers are most likely to stop using their services, enabling them to act to retain them before they leave.