Analyze multichannel purchasing behavior.
Start with hypotheses, formulate the questions you want to answer, and choose questions you’re ready to act on.
For example, you want to understand what share of your offline sales were influenced by online ad campaigns. Or, you may want to know at which stage in the funnel it’s best to encourage customers to make a purchase and which is the best channel to do so.
Prioritize the questions you want answered to decrease the number of subsequent tasks and start building an MVP version of your attribution dashboard.
Use apps, loyalty cards, promotions, contests, and similar methods to mine data from key company sources like Google Ads, Facebook, email marketing, mobile analytics, CRM and ERP systems, and track your customers’ journey online and offline.
- Use online apps to lead customers offline to be able to identify the same users across online and offline channels. For example, Polish furniture and decoration producer VOX, used an app where customers can design their own furniture. After they make the design, the app invites them for a physical consultation in the brand’s brick-and-mortar stores.
- If you have a mobile app and customers have it installed, place Bluetooth beacon devices in your physical store to track popular areas of a store and the products they pick up.
- Offer loyalty cards that must be activated online with an email address and are connected to a user ID and phone number.
- Send special offer emails that customers can subscribe to, or run promotions and contests that can be entered by signing up and providing contact information.
- Offer an extra month of warranty for registering the receipt online and engaging past purchasers to learn more about repeat buyers.
- Measure intent with event tracking with soft conversions like ROPO - researching products online and buying them offline - to get an idea if online visitors will actually visit a store.
- Connect activity on the website, applications, CRM, and POS with a unique user ID.
Collect data and use automated data flows with solutions like Google BigQuery or Amazon Redshift, and build or buy a service to connect all your systems that collect, store, and process data.
Automatically import data with services such as Cloud Dataprep by Trifecta. Use decent connectors like Stitch or Funnel.io.
For example, French women’s lingerie retailer Darjeeling decided to collect data about online sessions, offline sales, and order completion rates, and combine data about offline sales and user behavior on the website, taking into account order completion rates. Darjeeling used OWOX BI Pipeline to send behavior data from Google Analytics to Google BigQuery, then set up automatic data uploads from Google Ads to Google Analytics, as well as expense data to Google Analytics from various advertising sources. They also used a connector to send data from their CRM to Google BigQuery to calculate the order completion rate and avoid possible data losses.
user_id assigned to each user who signed in to the company’s website, to merge data about online sessions and order completion rates into a single view.
user_id is linked to the number of the customer’s loyalty card and stored in the CRM. When a user visits the website, their
user_id is sent to Google Analytics and Google BigQuery as a custom dimension. In BigQuery, it’s combined with two other keys:
For example, this has helped Darjeeling find out it takes up to 90 days for visitors to make a purchase decision after visiting the website. Darjeeling analysts combined this data by first looking at the
time keys from the table. Second, they selected data from online interactions in which orders were completed before the selected date. Third, they identified the channel groups for the sessions closest in time to the transaction date.
Create reports and dashboards for company management to evaluate the impact of online advertising on offline sales.
For example, establish how many customers visit the website before buying offline. Combine all data collected in BigQuery into a single table using SQL queries. Build reports in a company-friendly format with Google Data Studio to make use of this data.
Verify data quality with tools like Google Analytics setup audit.
Set the UTM tagging and other taxonomies, and have a
user_ID field to track customers across devices. Implement a measurement plan.
Plan activities like training and workshops, and create documentation to increase your team’s data literacy to understand metrics, how to use the tools, and understand the statistics that answer business questions.
- Are employees as curious and motivated as they should be when it comes to using data?
- What can I do to correct the negative and amplify the positive?
- Does the percentage of employees who use our self-service analytics tools match my expectations?
Build dashboards after defining the essential metrics that answer your questions, ensuring KPI calculation logic is transparent and approved by the team.
Create a prototype on paper or with prototyping tools to check the logic.
Don’t calculate metrics on dashboards, and don’t bend or manipulate data. Don’t try to make your visualization tool a computing one. All calculations have to be done beforehand and stored in a data set that your dashboard refers to.
Act based on the numbers your dashboard shows you, given that you get answers to the hypothesis questions you asked in the first place.
Check with employees to find out if they use your analytics system, whether it’s reliable, allows for collaboration, and properly integrates new data flows.
Maintain the system to ensure that data is always discoverable, clean, organized, and easy to work with. Survey employees to see if the system serves them. For example, ask questions like “Do you have the data you need to do your job effectively?” and “Are you able to get data in a timeframe that meets your business needs?”