Facebook Analytics Explained with Examples (Retention, LTV, Funnel, Users…)

We all have Facebook Analytics, but we do not know “How to use & what to measure on Facebook Analytics?”.

Facebook Analytics is an event-based analytical tool, unlike Google Analytics which a page view based tool. That’s why I would recommend using Google Analytics for tracking website data before the conversion and Facebook Analytics for after the conversion. This also reminds me of another blog that I wrote highlighting the difference between Google Analytics and Mixpanel. Mixpanel is also an event-based tool often preferred by marketers when there is a lot of post-purchase data generation.

How Facebook Analytics Works?

Facebook Analytics is an event-based tool. When you put the pixel on your website, for conversions, you also define some standard events such as Add to cart, Initiate Checkout, Purchase, etc. The same events on Google Analytics are treated through page views. That’s why Facebook Analytics is easier to measure during checkout and post-checkout.

Facebook Analytics Main landing page

When you log in to Facebook Analytics, there are basically two panels using which you get the most of the data. On the left, you will see the activity panel which contains the most of your metrics and at the top, it contains the dimensions using which you can filter the events and analyze the data. Yes, Facebook does consider standard as well as custom events in its analytics.

Top Event Filter

Instead of talking more about the theoretical stuff, I will show more practical things that you can achieve using Facebook through different test cases. The most useful feature that I find on Facebook Analytics is the top events filter. Though it sounds just a normal Filter but if you can master the uses of these filters, it can give the answer to a lot of questions easily.

At the basic level, I would suggest you learn at least these three things through the filter:

  • Create a multi-layered filter (User who bought in last 28 days using an iPhone with an average order value of 100 to 150)
  • Save a filter
  • Compare two filters

If you have mastered or at least learned to use the above three action items, then you are halfway almost there. The rest of the wisdom lies in coming up with different hypotheses and testing them against the data. I am going to add a few examples and I hope some of those examples or use cases would give you a started insight into how to make the most out this tool

What to Measure on Facebook Analytics?

  • Funnels
  • Percentiles
  • Lifetime Value
  • User behaviour
  • User activity
  • Cohorts etc.

Facebook Analytics Example: New User Activity

New User retention in Facebook Analytics

New User retention in Facebook Analytics

  • Select Retention from the left panel
  • Filter users based on the “New User Activity” event under the filter with purchase event equal to 1.

Retention Use Case Insights:

  • New users who do at least one purchase are engaged until the 12th week (3 months). After the 12th week, the retention rate flattens out to 5% on the average.
  • The new users who make at least one purchase seem to be engaged more every alternate until almost the third month. You can further look for specifics and make a strategy from there. For example,
    • Do they return often to check the status of their product?
    • Do they return to their account page?
  • Can we recommend another product with a personalized discount in their basket while they wait for their first product to be delivered?

Use Facebook Analytics for Retention

Overall new users Engagement

Overall new users Engagement

  • Select Retention from the left panel
  • Filter users based on the “New User Activity” event only

Retention Use Case Insights:

  • User retention drops drastically after the first and second week.
  • What keeps the user interested in the first and second week?
  • What can be done to keep the user engaged & retained?
  • On average, exactly when these users stop coming to the website?
  • What’s the number of average sessions that these users make before deciding to leave the page?
  • Can we talk to the most engaging ones?
  • Can we put a survey for the existing users to improve the engagement of our website?

How to Use Facebook Analytics for Lifetime Value (LTV)

LTV Cohort Facebook Analytics

LTV Cohort Facebook Analytics

  • Select Lifetime Value (LTV) from the left panel
  • No filter needed at the top
  • Select Paying users above the cohort table and time interval

Lifetime Value (LTV) Use Case Insights:

  • The Life Time Value of a purchaser increases by 50% in the next 6 weeks.
  • The possible reason could be the repeat customers who come back to buy the product again.
  • If all of the customers become repeat customers then in 6 weeks, the LTV will become double, however, not all the customers are returning to buy within 6 weeks. What can be done to further increase the LTV of a user in the same time period or in a short time period?
  • Should we offer them a discount on their next purchase? Shall we keep out marketing afloat for such users?
  • Should we remind them that their product might have been over and they need to rebuy it depending upon the product use life?
  • Should we cross-sale them?

Facebook Analytics Explained for Percentiles

Purchases percentile in Facebook Analytics

  • Select Percentiles from the left panel
  • Select people with at least one purchase in the event filter at the top

Percentiles Use Case Insights:

  • The top 10% of the users seem to bring 50% of the revenue alone with an average order value of 150. Top 5% of the users (Premiums) seem to bring around 30% of the revenue alone with an average order value of 250.
  • How could make them feel special? Do we say them thanks and send them some free products to try?
  • Do we remind/celebrate/surprise them on birthday, anniversary, valentine’s day, weekend party with them?
  • Do we personally care for them and their choices & orders?
  • This leans more towards email marketing.

Facebook Analytics Use Case for Funnels

Ecommerce Conversion Funnel in Facebook Analytics

Ecommerce Conversion Funnel in Facebook Analytics

  • Select Funnels from the left panel
  • Create a Funnel by selecting the following events
    • User activity
    • Add to Cart
    • Initiated Checkout
    • Purchases

Percentiles Use Case Insights:

  • 7% Add to cart is the bottleneck of the whole user funnel at the moment.
  • Why users are hesitant in adding to cart and why some users are taking more than a day to add to cart?
  • Is there any need to A/B test to different product pages separately on Mobile & Desktop?
  • Can we try sticky Checkout buttons on Mobile?
  • Can we A/B test button size, color, etc. for adding to cart on the individual product pages?
  • But why only around 45% of the audience who add to cart checks out (67% of 67%)

So, we also decided to use Google Analytics and check the page speed insights.

Site speed during checkout process

Site speed during the checkout process

And, to our surprise, we found that the cart page itself was very slow and faulty.

Facebook Analytics Funnels: Completion Time

time to purchase in facebook analytics funnel

time to purchase in facebook analytics funnel

Funnel Completion Time Insights

  • Top users complete their transactions within minutes.
  • What’s impeding the last 25% of users in checking out? Did we ever ask them?
  • Should we have a survey on user exit intent? Have we thought tools such as Hotjar or Survey Monkey for that>
  • Should we offer them a little more discount on exit intent to make the purchase?
  • Should we remind them of checkout?
  • Should we create urgency just like Amazon with a discount expiring in the next 10 minutes?
  • SMS or Email?

Overall Facebook Analytics Measurements

Pros:

  • From the lifetime Value (LTV) cohort table, we know that our users have the capacity to become more valuable by 50% in just 1.5 months even if do nothing.
  • People engage for a long time on the website after making a purchase
  • The premium and loyal customers, through percentile graph, are high paying customers

Cons

  • We need to fix the ToFu pages so that the audience can percolate through and we can have more add to carts.
  • TheSecond bottleneck is the checkout process funnel and the low through rate with high time to completion timeline.

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