Data, data everywhere.
Data Positively Affects Your Revenue Stream
It’s no secret that harnessing large volumes of data quickly and accurately during these phases:
- Pre-purchase (attract users)
- Conversion path (engage users)
- Retention (grow user base)
will positively affect your revenue stream.
The challenge is: In many cases consumer intent data outpaces the technological ability to act on it. It is a complex and pricey endeavor often available only to the largest of companies.
Marketing cloud companies have released a slew of complex products that attempt to allow marketers to harness this vast data volume. But customer data often is not continuously modeled so you may not connect properly with your customers.
Behavioral 360 User Segmentation and Micro-Targeting
Traditionally, marketers have lumped audiences into broad groups based on attributes like location or simple product category based intent.
A behavioral 360 user segmentation instead segments your users more precisely based on their actions. The data used for segmentation may be retrieved from several sources:
|Site Behavior / Purchase Variables||Environment Variables||Referrer Variables||Temporal Variables||CRM Variables|
|Customer/prospect||IP address||Referring Domain||Time of day||LTV data|
|New/ Return visitor||Country of origin||Campaign ID||Day of the week||Purchase History|
|Previous visit patterns||Time zone||Affiliate||Recency||AOV data|
|Previous product interests||Operating system||PPC||Frequency|
|Searches||Browser type||Organic search|
|Previous online purchases||Screen resolution|
|Previous campaign exposure||Device|
This micro-targeting however requires from marketers to know their audience niches and their needs. This is not always the case nor is it realistic when you are dealing with hundreds of products and categories.
The Rise of Artificial Intelligence (AI) in Marketing
Meanwhile, Artificial Intelligence (AI) is being touted as the next major wave of innovation. And it holds a ton of promise to allow marketers an easier and smarter path to revenue generation.
Machines can learn from past interactions and data, allowing marketers to help consumers not only with what they say they want, but to anticipate their future needs by connecting consumer interactions into one consistent and cross device stream of messaging.
Retroactive and Predictive Segmentation to Your Rescue
Armored with retroactive and predictive user profiles, the AI-enabled marketer instead may easily:
- Predict each user’s likelihood to perform any action
- Specify a set of personalized messages to engage with each user
- Automatically adapt individual user journeys in real time
- Display the right product, content, message or offer at the right time
How to Implement a Predictive Segmentation?
Online behavior can be shifted towards increased conversation rates by applying predictive segmentation to personalize user journeys.
An example: How does a retail company determine discounting on active shoppers to clear excess inventory? The company typically has no explicit data on the types of customers that react favorably to discounting. It will use its customer database and predictive modeling to identify who to offer discounts to, in real-time.
For AI to power smarter decision making it needs to operate itself on five central principles:
To learn, the machine must access rich customer data such as demographics and purchasing behavior. Using these variables, the AI based predictive marketing tool builds a statistical model that determines how predictive each variable is in terms of the answer the marketer is trying to learn.
You tell the AI based predictive marketing tool what you would like it to learn for you. For each question, the probability is calculated on the basis of answers up to that point. The machine looks for combinations of attributes that create a high level of certainty about the answer it is seeking.
Eventually, the probability is weighted one way or the other. You decide how confident they want the machine to be in its answer. You may say that once it’s 95% confident, it can stop.
The model automatically updates itself with the latest visitor information and ensures continued relevancy.
Users of your website or mobile app are notified of what is most relevant to increase the likelihood of conversion. Within an AI based predictive marketing tool, you set a behavior goal, execute it on live traffic and track progress.
Predictive Segmentation and Personalization Use Case: Barre3
Let us take a look at a use case revealing how predictive user segmentation and personalization help marketers to drive conversions and other important metrics.
Barre3 created a healthy lifestyle hub. Apart from having studios in 30 of the United States, Canada and the Philippines, they offer workout accessories, apparel and online courses.
The brand invests a lot in influencer and content marketing alike to boost their services. Guest authors share fitness and health related tips and tricks on Barre3’s blog.
Make Followers Become Subscribers Via On-Site Messaging
The goal of this use case is to
- Make new users
- Referred by an influencer
- Become subscribers to the online classes
- Based on individual user actions and intents
- Without affecting regular users
- Reducing CAC (customer acquisition cost) and LTV (Lifetime Value)
- Evaluating cooperation with the influencer via user analytics
How to Use External Influencer Data to Approach to the Right Users?
How could Barre3 utilize the intent data from external influencers in an efficient yet individual way while using the advantages of a predictive segmentation and personalization? The aim of this campaign also is tempt users to subscribe by offering a free trial period.
Collect and Store Data Using 360 Data Model
Identifiers are used to track and identify individual users across channels making it possible to adjust and adapt the database at any point of the user journey.
Before creating the campaign, we need to install the Intempt tracker on the website. The tracker is a code snippet similar to Google Analytics collecting and storing each user’s behavior (page views, clicks, form submits and other actions) so we can roll out personalized messages.
Next, we will import external third party data. Emilie Blanchard, one of Barre3’s influencers, will launch an email campaign for her subscribers pointing to her guest post on the Barre3’s blog:
Then we will import customer data from Barre3’s CRM indicating the customer lifetime value (LTV). We then combine and blend the influencer data with the CRM data using identifiers.
Create a Behavioral Segment
We then specify a target segment blending past (factual) behavior and future (predicted) behavior. In our case we may target users who
- Are identified
- Subscribed to Emilie Blanchard’s newsletter before
- Are considered to be high level customers (current LTV above $100.00)
- Are predicted to leave without subscribing to an online class
This is how our user segment would look like:
Deliver On-Site Behavioral Messages
A user personalization campaign may be easily created by adding
- A user segment
- A campaign goal (tracking the campaign’s success: “User subscribed”)
- One or more message copies
- Additional delivery preferences (5 seconds delay, trigger only on blog)
The final message could look like this:
For users influenced by Emilie with a LTV lower than $100.00 we may create another segment:
...to roll out a lower value offer (one instead of three months for free):
Because Itempt is tracking the individual user’s actions, the user’s actual LTV value can be used to
- Evaluate the influencer’s impact on the brand
- Adapt additional bonuses
- Encourage the influencer
- Override the LTV to keep the database up to date
- Get customer insights
Why a Message at This Time?
Users at this time
- Are browsing the blog
- Are identified
- Subscribed to Emilie Blanchard’s newsletter before
- Are considered to be high level or low level customers (current LTV <> $100.00)
- Are predicted to leave without subscribing
Boost Your Business Via Predictive Segmentation and Personalization
Target visitors based on their behaviour across channels while you still retain full control over your campaign.
If you are struggling with long customer journeys and fragmented visitor segments, this approach can be your tool to drive conversions.
Just be personal, in a smart way.