In 2015, Google released its Customer Journey to Online Purchase insights tool and there was much rejoicing. Marketers and communicators found great value in Google’s generic models for how customers made decisions with marketing inputs. However, many were concerned that its overly broad models – “business and industrial”, for example – could mean a B2B technology company or an industrial concrete company, two very different businesses.
What if a company’s data doesn’t exactly map to the generic model? Perhaps it’s a company with better than average email marketing skills. A few companies are likely to have community managers who create awareness and revenue on social networks. Google’s starting model tells us a little about an industry, but won’t help us improve a business. How can we then use this model and play to unique strengths and mitigate weaknesses for individual businesses?
We applied Google’s Customer Journey idea and paired it with data-driven models to help you understand what’s really working in your digital marketing plans.
To do this, we’ve blended an industry’s generic model with an organization’s unique data, using tools like Google Analytics, social media analytics, and advanced statistical tools. Our Data-Driven Customer Journey model helps you:
- Understand what channels deliver results
- Discover the sequence your customers take to purchase
- Learn whether a channel has the right messaging
- Reveal hidden opportunities to boost revenue and results
Below is an example of Google’s generic model compared to a model we analyzed for one of our clients:
Above, we see the generic model calls for social media to lead the purchase process, with display and paid search reinforcing the customer relationship in the early stages. The client’s actual path to purchase shows display initiating the relationship, but not very well. Our guidance to this client is to change their display messaging to align with the beginning of a relationship, and to remove ‘buy now’ calls to action from their social media postings.
We’ve tested our model against many different companies and industries, from B2C consumer goods to IT security firms. Our most recent examples include:
- A large online B2B company with multiple sub-brands wanted to understand their marketing opportunities and challenges – what should be kept, what they should leave behind and what new things they should try to increase sales.
- With a B2C SaaS client, our model helped plan their budget for the year based on performance metrics using analytics data and marketing automation data. We mapped their data to their goals and outlined a strategy for determining what they should do in 2016.
- We eat our own dogfood; year over year, using our model, we’ve increased total conversions by 103% and conversion value by 50%. Yes, we test all models on ourselves before offering the opportunity to clients.
Marketers and communicators are always looking improve results, to generate more awareness and revenue. With the data gold that exists in our marketing systems, it’s almost criminal not to make data-driven decisions to create better-informed budgets, more conversions, more strategic plans and a more successful year. We hope our paper helps to showcase the customer journey process and how it can be applied to your company’s data to boost your results.
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Account Manager, Integrated Services / Marketing Strategist