Understanding the Customer Journey Through Data
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How much do you really know about the journey that a typical customer of your business takes to get to the stage of making a purchase? What are the main paths that customers follow, and what influences those paths?
In an increasingly digitalised world, understanding the customer journey is becoming more important. By understanding the customer journey, you can optimise your advertising and marketing efforts to ensure they are as effective as possible. Data science can be an invaluable tool in doing that.
Why Is Understanding the Customer Journey Important?
Even simple purchasing decisions can involve complex customer journeys. To understand this, think about all the different ways that customers can interact with your brand:
- Desktop web search
- Mobile web search
- Mobile app if your company has one
- Social media post
- Paid ad – paid search, display ads, social media, sponsored post, etc.
- Email newsletter
- Loyalty scheme message
- Offline advertisements – television, billboard, radio, etc.
- In-person interactions at conferences, exhibitions, sales presentations, etc.
- Phone calls
- Online chat
- Address/location searches on Google maps
- In-store visits
- Adding items to an online shopping cart
- Completing a purchase in store or online
How many of the above could a customer encounter or instigate prior to making a purchase?
What order do customers follow? Will they find you on paid search first, for example, before then getting to know more about your products or brand? Or do customers already know about your products and brand and interact with you in different ways Having the above information as well as anything else you can learn about the customer journey will improve marketing and customer service in your business.
The Problem with Understanding the Customer Journey
So understanding the customer journey is important but there is an important problem to solve before this can happen. The problem is complexity.
After all, you can access data on the interactions customers and potential customers have with your business from a wide range of sources.
Here are some of the most common sources of data that are probably available to your business today:
- In-store sales
- Online sales
- Web browsing
- Customer service
- Customer surveys
- CRM systems
- Advertising platforms like Google AdWords
- Third-party marketing channels like social media platforms
- Marketing automation tools
- Email marketing
- Customer loyalty schemes
- Mobile app
You may also have other sources of data not on the above list.
In other words, access to data is not the issue as you have loads of data at your fingertips. The data is complex when looked at collectively, however, so how do you make sense of it? How do you distil data from the above sources into a defined customer journey? Today’s customer journeys can be complicated and mix the real world with digital, and understanding which channels are the most important ones can be very challenging.
One of the key problems is attribution modelling. That is, understanding which channels are the ones that contribute the most to someone’s purchasing decision. Another related problem, is journey restructuring. How can you adapt a user’s journey (by adding or removing channels), in order to make it more efficient. Finally, another very interesting area of application is personalisation and contextual advertising. If we know, for example, that a user participates in the journey mostly through ads on particular websites, how can we adapt the ad content to make them more prominent?
The Solution: Data science for marketing
The solution to defining the customer journey through data is to take a structured approach. This means using tools to analyse the data and then pull it into a usable and understandable format. In other words, data is the source of the customer journey, but data science is the solution to creating the definition.
There are many techniques from data science like Bayesian statistical modelling or hidden Markov models that can be used in order to model a user’s journey and predict how a user would behave if the journey was to slightly change.For example, make sure to check out this tool I created, based on Bayesian statistical modelling, to help you understand which ads perform better.
Data science is becoming more and more popular in marketing, and this is the reason I also created a Meetup in London, so that all of us, who are interested in this area, can join and exchange knowledge. If you are interested in this area, make sure to join our next event!