Would you like to know how much they’re spending, when they’re spending, why they’re buying with the competition and not you, what drives their purchase behavior and what steps you can take to capture a greater share of wallet.
This consumer intelligence is fundamentally obtained and analyzed through the creation of a 360-degree customer profile platform, which your organization can create without a lot of effort and without hiring expensive consultants or data scientists.
For many organizations, creating a 360-degree platform which they can feed data into can represent the largest challenge. How do we collect the data? Where will it come from? How do we store it, keep it structured and keep it up to date? Who will own and maintain the data set internally? What privacy protections do we need to put in place, and how will our new market intelligence affect existing business?
While I won’t go into in-depth detail on all of these questions, it is important to note that having a full 360-degree view of your customers is becoming the new norm.
For many organizations, the biggest risk is being late to the party. Not having up to date, real-time information on your customers’ engagement, spending and transactions with the competition means your organization is at a significant disadvantage. How do you know what your share of wallet is if you don’t know how much they’re spending with the competition? In essence, the opportunity cost of not taking action to create a platform is the biggest expense.
In an early article, titled ‘How to Commercialize your Frequent Flyer Program Data,’ I spoke about how the NSA doesn’t yet run a commercial division where it sells personal user data, and you’ll need to create your own. Engineering a platform to work for your economic goals is key.
The first step is to understand everything about your customers; from transactions, engagement propensity, who they are connected to in their social network; and combining all of this information in a structured format so that you can easily cross-reference and compare it when creating models.
Put on your creative hat and get to work. Consider the channels, partners, and process a user would go through to transact with your competitors.
For example, let’s say you own a small hotel and want to know if any of your previous guests have spent more than $200 at any hotel within a 2-mile radius over the past 12 months.
To answer this question, you would need to be capturing data from external sources which will help you understand the problem.
Here’s a few to get you thinking:
There is no one single magic source to collect data to create a 360-degree view of your customers. You’ll be capturing multiple data points, from multiple vendors and running services on your side to ensure ephemeral data remains relevant.
Once you’ve started capturing and storing external information, you’ll want to create meaningful insights to be leveraged for commercial gain.
Now is the point where business intelligence meets data science. If you’re capturing all the right data, you’ll want to understand the propensity of customers buying your product over competitors, and what other factors may have influenced their decision to transact with you, and your competitors at any point in time.
For example, by overlaying weather data on specific transaction dates it’s possible to understand if hotel guests would have considered your hotel over the competition or they were forced to stay somewhere based on weather or location.
Best-selling author and big data expert Bernard Marr gives an example – “The US economy hotel chain Red Roof Inn who, during the record-setting winter of 2013/2014, realized the huge value of having a number of hotels close to major airports at a time when flight cancellation rate was around 3%. This meant around 90,000 passengers were being left stranded every day. The chain’s marketing and analytics team worked together to identify openly available public datasets on weather conditions and flight cancellations. Knowing that most of their customers would use web search on mobile devices to search for nearby accommodation, a targeted marketing campaign was launched, aimed at mobile device users in the geographical areas most likely to be affected. This led to a 10% increase in business in areas where the strategy was deployed.”
StayAngel, the hotel price monitoring service which closed in 2016, captured transactional data from hotel guest stays, and using machine learning, were able to predict how much a hotel guest would pay for a room at any given hotel – before they booked a room. It is said to have increased ADR by up to 30% without any change in hotel operations. This was only made possible by having a full view of each hotel guests share of wallet across all the major chains.
Finally, the underlying key to designing, engineering and implementing your 360-degree customer view platform is to think with the end in mind. What does your organization want to achieve, and how will this intelligence be used in a positive way to bring new value and enhance the customer experience.