# Introducing a New Framework for Mobile Engagement: Acknowledgment — Interest — Conversion (AIC)

- Link: https://phiture.com/mobilegrowthstack/introducing-a-new-framework-for-mobile-engagement-acknowledgment-interest-conversion-aic-abdecb073940/
- Published: 2019-07-18T11:49:02+01:00
- Author: Andy Carvell

_This article is co-authored by Andy Carvell, Kevin Bravo and Tessa Miskell, based
on a data investigation project conducted by _[_Phiture_](https://phiture.com/)_
in partnership with _[_CleverTap_](https://clevertap.com/)_._

## ⌊Introducing a New Framework for Mobile Engagement- Acknowledgment — Interest — Conversion (AIC)⌉⌊Introducing a New Framework for Mobile Engagement- Acknowledgment — Interest — Conversion (AIC)⌉

Table of Contents

 * [Introduction](https://phiture.com/mobilegrowthstack/introducing-a-new-framework-for-mobile-engagement-acknowledgment-interest-conversion-aic-abdecb073940/#introduction)
 * [All Activity is not Equal](https://phiture.com/mobilegrowthstack/introducing-a-new-framework-for-mobile-engagement-acknowledgment-interest-conversion-aic-abdecb073940/#all_activity_is_not_equal)
 * [Understanding Engagement](https://phiture.com/mobilegrowthstack/introducing-a-new-framework-for-mobile-engagement-acknowledgment-interest-conversion-aic-abdecb073940/#understanding_engagement)
 * [The Acknowledgement Layer](https://phiture.com/mobilegrowthstack/introducing-a-new-framework-for-mobile-engagement-acknowledgment-interest-conversion-aic-abdecb073940/#the_acknowledgement_layer)
 * [The Interest Layer](https://phiture.com/mobilegrowthstack/introducing-a-new-framework-for-mobile-engagement-acknowledgment-interest-conversion-aic-abdecb073940/#the_interest_layer)
 * [The Conversion Layer](https://phiture.com/mobilegrowthstack/introducing-a-new-framework-for-mobile-engagement-acknowledgment-interest-conversion-aic-abdecb073940/#the_conversion_layer)
 * [Implementing the framework: creating your own AIC Snapshot](https://phiture.com/mobilegrowthstack/introducing-a-new-framework-for-mobile-engagement-acknowledgment-interest-conversion-aic-abdecb073940/#implementing_the_framework_creating_your_own_aic_snapshot)
 * [Setting up tracking to monitor the layers](https://phiture.com/mobilegrowthstack/introducing-a-new-framework-for-mobile-engagement-acknowledgment-interest-conversion-aic-abdecb073940/#setting_up_tracking_to_monitor_the_layers)
 * [Discard all other tracked events from the analysis](https://phiture.com/mobilegrowthstack/introducing-a-new-framework-for-mobile-engagement-acknowledgment-interest-conversion-aic-abdecb073940/#discard_all_other_tracked_events_from_the_analysis)
 * [Creating an AIC Snapshot](https://phiture.com/mobilegrowthstack/introducing-a-new-framework-for-mobile-engagement-acknowledgment-interest-conversion-aic-abdecb073940/#creating_an_aic_snapshot)
 * [Generating deeper insight using the AIC Framework](https://phiture.com/mobilegrowthstack/introducing-a-new-framework-for-mobile-engagement-acknowledgment-interest-conversion-aic-abdecb073940/#generating_deeper_insight_using_the_aic_framework)
 * [Trends over time](https://phiture.com/mobilegrowthstack/introducing-a-new-framework-for-mobile-engagement-acknowledgment-interest-conversion-aic-abdecb073940/#trends_over_time)
 * [Ratios between layers](https://phiture.com/mobilegrowthstack/introducing-a-new-framework-for-mobile-engagement-acknowledgment-interest-conversion-aic-abdecb073940/#ratios_between_layers)
 * [Elasticity](https://phiture.com/mobilegrowthstack/introducing-a-new-framework-for-mobile-engagement-acknowledgment-interest-conversion-aic-abdecb073940/#elasticity)
 * [Applying a Funnel Perspective](https://phiture.com/mobilegrowthstack/introducing-a-new-framework-for-mobile-engagement-acknowledgment-interest-conversion-aic-abdecb073940/#applying_a_funnel_perspective)
 * [Turning Insight into Action](https://phiture.com/mobilegrowthstack/introducing-a-new-framework-for-mobile-engagement-acknowledgment-interest-conversion-aic-abdecb073940/#turning_insight_into_action)
 * [Summary](https://phiture.com/mobilegrowthstack/introducing-a-new-framework-for-mobile-engagement-acknowledgment-interest-conversion-aic-abdecb073940/#summary)

## **Introduction**

‘Mobile Engagement’ is a nebulous term; it gets bandied around in many situations,
often with scant regard for its definition.

In the broadest of terms, Engagement can be considered the act of the user interacting
with / using the product. It’s often brought up along with Retention, so perhaps
it’s also useful to differentiate these two terms:

_Retention measures whether a user returns to the app in a given time period_

_Engagement measures _**_what users do _**_when they are in the app_

In this article, we propose a framework (the ‘Acknowledgment-Interest-Conversion
Framework, or AIC for short) for measuring and analyzing engagement that might prove
helpful when monitoring the usage of an app and inform growth initiatives designed
to increase meaningful engagement.

## **All Activity is not Equal**

Consider two users: User A opens Instagram on Monday, browses her feed, likes two
posts, replies to a comment, then posts a photo with some accompanying hashtags.
User B opens Instagram, skims their feed, and closes it again after a few seconds.

It’s clear that User A demonstrated significantly higher engagement on Monday. However,
both users are considered retained and — by many traditional measures — active and
engaged.

Both users would be considered part of the Daily Active Users (**DAU)** metric on
this day. They would also qualify both users as Monthly Active Users (**MAU**) within
a 30-day window of the day in question and Weekly Active within a 7-day window.

Some companies opt to combine engagement and retention into key metrics such as 
Monthly Active Listeners ([SoundCloud](https://www.soundcloud.com/)) or Weekly Active
Pinners ([Pinterest](https://www.pinterest.com/)) and may even decide to make such
a measure their [North Star Metric](https://growthhackers.com/articles/north-star-metric).

While many metrics (e.g. DAU, MAU) simplify engagement to binary terms (counting
a user as either active / engaged, or not, in a given period), it’s important to
understand that _engagement is a continuum_, ranging from low-value to high-value
actions. A user may traverse this spectrum rapidly within the space of a few sessions,
or remain stubbornly stuck in a low-engaged state for days, months, or years, without
necessarily abandoning the app.

## **Understanding Engagement**

Engagement can be considered in the form of a hierarchical pyramid, with different
actions falling into different layers:

⌊AIC Framework - a more nuanced view of engagement ⌉⌊AIC Framework - a more nuanced
view of engagement ⌉

_AIC Framework: a more nuanced view of engagement_

The pyramid, as we’ve defined it, consists of 3 layers of increasing engagement 
value, with a ‘core action’ at the top, representing the highest-value action a 
user can take within the app (in e-commerce, this would be a purchase, for social
apps, it’s likely to be creating a post, for a todo-list app, it would be creating
to-dos).

By classifying key user actions into one of these three layers, we can use the AIC
framework to analyze user behavior at aggregate, cohort, or individual user level
in the context of the value this engagement contributes to the business.

## **The Acknowledgement Layer**

This is the lowest level of user engagement. Actions the user might take that would
be classified as ‘acknowledgment’ actions include opening the app and interacting
with push notifications or email messages received outside of the app. These actions
in themselves don’t represent a strong intent. This would include users who were
effectively retargeted through advertising and deep-linked back into the app.

_Events this layer contains:_

**User opens the app**

**User opens a Push Notification **/ **Email**

## **The Interest Layer**

This classification would cover the majority of user actions within most apps. Any
action the user can take that demonstrates real intent/interest in key app content,
features, or other users (in the case of social platforms) would be classified as
an Interest Action.

_Events this layer contains:_

Deliberate, user-initiated, high-intent actions such as: tapping on feed items/products/
content to ‘consume’ these items, initiating search queries, use of key features
within the app.

_Events this layer does not contain:_

Automatically-generated events (not requiring user input), Scrolling, Switching 
between tabs, Changing settings or other ‘admin activities’, and anything except
deliberate access to content or interaction with key features.

## **The Conversion Layer**

In the majority of cases, it should be possible to designate one specific user action
as being a core action, both to the user experience and also to the business of 
the app publisher. This Conversion event need not be a monetization event such as
a purchase, but it should be closely aligned to the ultimate success of the product.

The Conversion event delivers value to the user and hence makes good on the value
proposition of the app. In some apps, this core action will be taken very regularly
by a significant portion of users (e.g. posting on Instagram), whereas in others(
e.g. travel booking) it may happen more rarely, among a smaller but highly-valuable
subset of the user base.

Events this layer contains:

_Only the Conversion event: Purchase, Create a Post, Do a training session (fitness
app example), Book an Appointment, Request a Viewing, etc._

_Events this layer does not contain:_

Any other events

## **Implementing the framework: creating your own AIC Snapshot**

Any framework is only useful if it aids conceptualization. Any framework involving
KPIs or metrics should also ideally satisfy some additional criteria:

 * The framework delivers value over established approaches
 * The approach is easy to explain within the company and to implement

In the case of AIC, there is some inherent tension between these two aims: our research
and analysis to date show that it can provide valuable, actionable insight, albeit
with greater complexity than some traditional metrics.

For more on different levels of engagement and the concept of a Core Action (the
action we track in the Conversion layer), we strongly recommend checking out Sarah
Tavel’s [Hierarchy of Engagement](https://medium.com/@sarahtavel/the-hierarchy-of-engagement-expanded-648329d60804)
framework, [this post](https://www.reforge.com/blog/retention-engagement-growth-silent-killer)
from the ever-awesome [Reforge](http://www.reforge.com/) blog and these [lessons learned at Pinterest](https://caseyaccidental.com/one-key-metric-victim)
from Casey Winters.

The AIC model is our attempt to create a standardized approach to stratifying and
analyzing the engagement of active users in a mobile app context. We leave it for
the reader to decide whether the framework is interesting enough to invest the additional
effort to track and analyze versus metrics such as DAU, MAU and single event counts.

## **Setting up tracking to monitor the layers**

In order to track engagement and use the framework, it’s first necessary to consider
the key actions a user can take within the app and to classify them as either Conversion,
Interest, and Acknowledgment.

Each of these actions should have a corresponding Event being tracked in analytics.
In a typical analytics tracking setup, there will be a lot of events tracked, including
many things that can be excluded from AIC analysis; it’s simpler to set up if you
only consider the key intent-driven actions that the user can perform.

_Since a user only has to trigger one of the Layer’s events to be counted in that
layer of the pyramid, any additional events triggered will not affect the result;
this is roughly analogous to the concept of being counted as ‘active’ in a given
period (e.g. monthly) regardless of the frequency of use within that time period._

Let’s take the example of [Fishbrain](http://www.fishbrain.com/), a social fishing
app. Fishbrain users can ‘log’ catches, posting a photo and other accompanying information
about the fish they caught. Logging catches is considered to be the Conversion event,
since much of the other engagement on the platform stems from social interactions(
likes, comments, etc.) with a social feed, which mostly consists of logged catches.

⌊Fishbrain app⌉⌊Fishbrain app⌉So, for Fishbrain, we can define the layers of our
AIC model as follows:

 

**Conversion**

_Log Catch event / Create Post events_

 

**Interest**

_Like, Comment, Share, Search, clicking on items in the feed, or in the Gear and
Discover tabs._

 

**Acknowledgment**

_Opened the app, Opened Push / Email_

Note that, in this framework, interactions with lifecycle messages and activity 
notifications are classified in the lowest level of engagement: Acknowledgment.

This keeps us honest; if these messaging campaigns are not leading to a decent amount
of genuine (Interest or Conversion) engagement, the messaging is probably not relevant
enough and could even be counterproductive. As such, we count interaction with a
message as very low-intent engagement, but it still counts for something.

## **Discard all other tracked events from the analysis**

All other events (of which there may be many: server-side events, screen events,
interactions with menus or other low-level engagement) can be omitted from the analysis
since they don’t represent significant, intent-based engagement.

We recommend being highly selective with the events classified as Interest. A good
rule is to only track things where the user is actively clicking on items, adding
things to a shopping basket, actively consuming content and similar deliberate actions
that represent genuine intent.

_If a user doesn’t need consciously decide to do a specific action for it to be 
tracked as an event, this event should not be included in the Interest layer_

The table below details some examples from popular app categories, to give an idea
of what kind of events would be in the Interest and Conversion layers for these 
apps (the Acknowledgement layer is the same for all apps).

⌊Examples of events for each Layer for some common app categories (available here
as a spreadsheet guide)⌉⌊Examples of events for each Layer for some common app categories(
available here as a spreadsheet guide)⌉

_Examples of events for each Layer for some common app categories (available here
as a [spreadsheet guide](https://docs.google.com/spreadsheets/d/14HBReEpFQZBe-ujtZektBqzIKqgauC7m5IWVawAzT8U/edit#gid=0))_

## **Creating an AIC Snapshot**

Once the events that will contribute to each of the layers have been decided, it’s
possible to generate a snapshot of engagement for use with internal dashboards, 
or for trend analysis.

Each layer of the pyramid is calculated according to the number of users who trigger
an action associated with this layer. i.e. if precisely 100 users opened the app
in the past month, the Acknowledgement layer would have a size of 100.

Layers are not mutually exclusive; the opposite is true. In order to trigger a Conversion
event, a user will certainly open the app (Acknowledgment) and — in the vast majority
of apps — register some Interest first by clicking on some content or otherwise 
displaying Interest-level engagement. It’s not strictly a funnel, but it’s normal
for all users counted in the Conversion layer to be also counted in Acknowledgment
and Interest.

The AIC Snapshot for a given period e.g. the past month would, therefore, look something
like this:

⌊An example monthly AIC Snapshot⌉⌊An example monthly AIC Snapshot⌉

_An example monthly AIC Snapshot_

Having such a snapshot visible on team dashboards, refreshing daily, would provide
an at-a-glance measure of the activity and the relative health of the app. It might
also help to catch some issues early i.e. if a bug is preventing some users from
doing the core action (Conversion event), it will be immediately obvious from an
AIC Snapshot that something is wrong, whereas DAU numbers would remain relatively
healthy initially.

DAU should correspond very closely to the number of users we see in the Acknowledgment
layer in a daily snapshot. If they open the app, they’re counted in this layer, 
so the framework essentially incorporates DAU, as well as two increasing levels 
of higher-intent activity in the Interest and Conversion layers.

_The AIC Model is most useful as a Dashboard Metric, providing a snapshot of engagement
and active user composition. To dig deeper into trends teams could use Investigative
Analytics techniques_

## **Generating deeper insight using the AIC Framework**

Once the key events have been categorized and the metrics for Acknowledgement, Interest
and Conversion can be calculated for any given period from historical event data,
some further analysis can be applied:

## **Trends over time**

Observing how the layers grow or shrink over time gives an indication of how well
the app is performing: ideally, the absolute numbers show an upward trend (user 
growth) and the percentage of users in the Interest and Conversion layers also increases
over time. These trends should also be observed in other company metrics, but one
could get a good sense of overall health from the AIC trends alone in a dashboard
view and then dig deeper using [Investigative Analytics](https://mobilegrowthstack.com/mobile-app-analytics-519f5719e283)
to identify which precise events are contributing to the growth or decline of numbers
in each layer and to perform segmentation or funnel analysis to identify the root
cause of the change.

Note that changes won’t always come from User Acquisition, growth team or product
team initiatives: external factors such as seasonality and market competition can
also be powerful influences on your AIC Snapshot trends.

⌊Trends over time show seasonality, content:inventory-driven effects, user acquisition
spikes, and more⌉⌊Trends over time show seasonality, content:inventory-driven effects,
user acquisition spikes, and more⌉

_Trends over time show seasonality, content/inventory-driven effects, user acquisition
spikes, and more_

## **Ratios between layers**

It’s useful to understand the ratio of unique users in Conversion, Interest and 
Acknowledgment respectively. These ratios typically show less volatility than the
absolute numbers and vary quite a bit for different types of apps; social apps and
content-based apps typically have quite a large Interest and smaller Conversion 
layers (so ratio can look something like 3:2:1 from Acknowledgement:Interest:Conversion),
whereas some simpler and more transactional apps typically tend to skew more towards
core (for example a 3:1:2 type ratio).

Just as you can observe overall numbers and %ge of the total unique users in each
layer (as described above), you might also want to track how the ratio changes over
time. If you see that scaling user acquisition or CRM campaigns just fattens the
Acknowledgment Layer (i.e. the ratio increases at the bottom of the pyramid), this
can be an indicator that these efforts are ultimately ineffective.

## **Elasticity**

Those readers who studied economics will be familiar with the concept of [price elasticity of demand](https://en.wikipedia.org/wiki/Price_elasticity_of_demand)(
sometimes known as price sensitivity), whereby the effect of price changes on the
demand for specific goods or services is explored. The concept of elasticity/sensitivity
can be explored in relation to other combinations of independent and dependent variables
and at Phiture we have found this analysis technique useful when the AIC Model is
applied.

Understanding how much Interest or Conversion is likely to grow if you are able 
to grow Acknowledgment (i.e. through increased User Acquisition, or sending a higher
volume of push notifications or Emails, for example) can inform growth strategies;
more on this later.

⌊Exploring Correlation and Elasticity between Acknowledge Metric and Conversion 
Metric ⌉⌊Exploring Correlation and Elasticity between Acknowledge Metric and Conversion
Metric ⌉

_Exploring Correlation and Elasticity between Acknowledge Metric and Conversion 
Metric_

In the above example, we see a pretty strong correlation between Acknowledge and
Conversion and the model indicates that an increase in the size of the Acknowledgement
layer of 1% could deliver a 1.12% increase in Core (assuming the model holds; it’s
just a prediction, based on historical data).

## **Applying a Funnel Perspective**

Applying a funnel view to see how long users (either individually, by segment, or
on aggregate) are taking to move between layers can provide additional insight into
how long it takes users to progress between the layers of engagement.

Note: a classic funnel view expects that users will progress in a strict order between
funnel stages, so if you set up a funnel that requires users to complete any Acknowledgement
action, followed by any Interest action, followed by any Conversion action, your
results will exclude users who jump straight from Acknowledgment to Conversion, 
which may be possible — or even common—in some apps.

# ⌊A-I-C Funnel - Last 6 months ⌉⌊A-I-C Funnel - Last 6 months ⌉

## **Turning Insight into Action**

As with any metrics, these numbers in dashboards, trend charts or elasticity analyses
are only useful if they provide some meaningful insight that informs the next actions.

Since the AIC Framework is designed to provide a high-level snapshot of overall 
engagement health, its metrics (Unique Users in Acknowledgement, Interest, and Conversion
for a given time period) are Outputs, rather than Inputs. _This is the case for 
pretty much all Dashboard-Level metrics_.

Teams shouldn’t aim to influence Output metrics directly, working instead on input
levers and sub-metrics that have been identified by growth and product teams. For
a great explanation of Input vs Output metrics and how to identify growth levers,
[check out Brian Balfour’s article on this topic over at the Reforge Blog](https://www.reforge.com/blog/north-star-metric-growth).

This doesn’t mean that Dashboard KPIs do not provide actionable information: quite
the opposite. They give an early signal of problems and opportunities (especially
when combined with trend data) and provide a signal that can and should be explored
at a deeper level using investigative analytics (running queries and building charts
to understand what exactly is changing and why).

At Phiture, we have found that applying the AIC Framework to apps across various
categories provides a deeper level of visibility in a company-level dashboard view
than the typical MAU or DAU metrics. Changes in layer ratios can raise important
questions that would not otherwise be asked and triggers investigations, deep-dives,
hypothesis generation, and experimentation.

Let’s get into specifics: below are some examples of hypotheses that might arise
from applying AIC and the next steps a team might take to explore them.

⌊Turning Insight into Action using the AIC Framework ⌉⌊Turning Insight into Action
using the AIC Framework ⌉

_Turning Insight into Action using the AIC Framework_

## **Summary**

 * AIC is a framework that breaks down active users in a given period by the** level
   of engagement** they have displayed in that period
 * AIC is not as simple to apply as basic MAU or DAU active user metrics, but stratifying
   engagement into layers provides deeper insight into user activity and de-emphasizes
   basic app-opens as a signal of app health.
 * Applying the framework to a specific app _forces the team to think_ about which
   events constitute Conversion actions and which ones demonstrate a genuine level
   of Interest.
 * Including notifications + emails as part of the Acknowledgment, layer keeps us
   honest about whether they really work: A good indicator that a CRM program is
   effective is when a higher correlation and elasticity between Acknowledgement
   and Interest Metrics is observed when filtering for opted-in users than those
   not receiving emails and pushes.
 * Observing the relative changes in Acknowledgment — Interest — Conversion over
   time provides a useful indicator of the health of the app. Changes will show 
   the effectiveness of product and marketing activities, combined with external
   factors including seasonality.

In subsequent articles, we’ll go into detail with worked examples of AIC in action
and how the analyses we’ve described have led to actionable insight in real-world
examples.