Demystify customer decision-making with the right tracking tools and attribution models.
To understand the levers that influence customer decisions, you need to build a comprehensive chronology of your customers' interactions with your company. Apply the right attribution model to that data, and you'll be able to optimize marketing, sales and support efforts, as well as personalize every customer experience for maximum effect.
If you’re reading this article, there’s a good chance your company conducts many of its operations online or in ways that can be recorded digitally. Depending on your industry, your customers may interact with your company across any of the following platforms:
Website or mobile app event tracking
Customer relationship management tools
Assembling a full chronology of the customer journey from first touch to purchase allows you to understand the inflection points that make or break a potential purchase.
The exact steps in a customer journey may differ based on your industry or business model, but here’s a general representation:
Discovery – Customer realizes they have a want or need
Research – Customer compares vendors and products
Engage – Customer enters your (virtual or brick-and-mortar) storefront, browses and speaks with your sales staff
Purchase – Customer purchases the product or service
Retain – Customer returns to the vendor for future purchases
Suppose you run an ecommerce store. A customer journey might look like so:
A customer learns of a new type of product through their acquaintances and Googles it.
The customer arrives at your website via the banner ad and begins reading reviews and browsing your blog. Every page the customer visits on your site is recorded by your event tracking software.
The customer adds items to their cart and makes an account on your site. Your ecommerce platform records the prospective transactions.
The customer abandons the cart for a few days as other priorities draw their attention, but is reminded of it by your email marketing software. The customer clicks on a CTA to complete the order. The email marketing software records this interaction.
The customer completes the order. Both the ecommerce platform and online payment processing platform record the transaction.
A week or so later, the customer leaves a review on your company’s social media profile.
Note how the steps above spanned six distinct platforms operated by your company: social media advertising, website event tracking, ecommerce, email marketing, payment processing and social media. To build a chronology of a customer’s interactions, you must find a way to put the relevant records into one environment and attribute them to the same customer.
As the example above illustrates, the customer journey can get quite complicated as customers traverse various platforms — to say nothing of the fact that they routinely change devices (e.g., from smartphone to desktop) and networks (e.g., from coffee shop to office).
There are no perfect solutions, but you can use several identifiers to distinguish between customers, devices and campaigns across web-based activities.
IP addresses. IP addresses are unique at the network level, so all web-connected devices in the home or office might have the same IP address. If you are a B2B company and have engaged the services of a market research company, there is a chance they can associate an IP address with the name of a company.
Cookies. Cookies are tags assigned to a browser session.
User agents. User agents provide information about a user’s browser, operating system and device.
Email or social media accounts. If your users are given the option to register easily through email or social media, you can use such accounts as identifiers. You will have to determine the tradeoff between the convenience, to you, of requiring registration and login, and convenience to the user of using your website without an account.
UTM extensions. UTM extensions can be used to distinguish different sources of traffic. A link to a page from social media might be tagged with character suggest.
Once you have assembled a chronology of your customers’ interactions with your company, you need to determine which steps in the process mattered most. There are several traditional customer attribution models, each assigning different weights to different stages of customer interactions. The simplest attribution models are single-touch, and only require you to be certain of the first or last interaction your customer has with your company.
Last-touch attribution gives 100% of the credit for a sale to the last interaction between the customer and your company. It is the default approach used by marketers and the simplest to implement — all you have to know is the last thing the customer did before purchasing.
First-touch attribution gives 100% of the credit for a sale to the first interaction between the customer and your company. Like last-touch attribution, it is suitable to cases where your company has low brand recognition or a very short sales cycle.
U-shaped attribution, also called “position-based attribution,” gives the lion’s share of credit to the first and last interactions, while dividing the remainder among the other interactions. It allows the interactions that are generally considered the most important — the first and last — to be strongly considered, without ignoring the rest.
Suppose the customer had four recorded interactions with your company. The first and last interactions might each receive 40% of the credit, while the two middle interactions would receive 10% each.
It could also be 50/0/0/50 if you don’t care at all about the middle interactions.
Linear attribution is strictly agnostic, and assigns equal weight to every interaction. It is a good approach if you don’t have any prior, compelling beliefs about the importance of any particular interaction.
Decay attribution gradually assigns more weight the closer an interaction is to the last. It is best suited to cases where a long-term relationship is built between your company and the customer.
Customer analytics does not end with the models mentioned above. More sophisticated custom models, built off of Markov chains or survival modeling, are a next step. It never hurts to sanity-check quantitative work with the qualitative step of simply asking your customers why they do what they do.
Given the volumes of data generated by growing numbers of apps, platforms and devices, it's hard enough to understand customer decisions when the data is in one place. Without an automated data integration tool like Fivetran, the task can prove insurmountable.
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