Optimove works by harnessing customer behaviour and applying an algorithm that accurately predicts how offers or incentives will influence customer response, spending and interactions with a site.
For many businesses, this type of technology automation is the missing link needed to make email marketing personalisation possible.
In this review we will take a look at:
- How Optimove Works
- What Makes Optimove Necessary
- Benefits of Using Optimove
- Setting Up Email Campaigns
- The Optimove Interface
To find out more about Optimove why not request a demo?
How Optimove Works
Optimove works by integrating with an existing client data base, CRM and/or sending application.
Customer data, such as sales, purchases and transactions are extracted daily from the client database using an ETL (extract, transform and load) application.
The data can then be divided into segments or target groups by the user.
Optimove offers standard data models for different industries, reflecting its current customer base: online gaming (bingo, casinos, poker, sports betting, etc.), foreign exchange trading, and ecommerce.
As it’s primary focus is as a retention tool Optimove requires that the data has already been coded with customer IDs.
The results are then presented in a common table and proprietary schema.
Once extracted Optimove can then create customer models along various parameters throughout their lifetime cycle.
The email subscribers list can then be modelled as follows:
- Lifecycle Stages – New, Active, At Risk, Churned Customers
- Segmentation – Persona, Geo-Location, Preferences, Activity
From these attributes Optimove can assign customers to “microsegments” that cluster analysis has found will behave similarly.
It’s important to understand that these “microsegments” represent a current customer state that will change over time. In other words each customer will belong to different microsegments at different stages in their lifetime cycle.
This provides the foundation for Optimove’s key mertices – lifetime value and churn predictions.
In addition Optimove provides cohort analysis reports, comparing performance of customers who joined during different time periods. This is yet another important type of information that is not always available.
What Makes Optimove Necessary?
All email lists lose subscribers over time.
This rejection rate is estimated to be around 25-30% per year.
In email marketing circles this drop off rate is referred to as churn or attrition rate.
The savvy email marketer is able to play with this number to optimise their email subscribers’ list.
In so doing they are looking to decrease the churn rate.
Which in effect means that they have the potential to increase the average customer lifetime value (CLV) by 25-30%.
So, ask yourself – what would it be worth to your business if you could increase CLV by 30%?
The value of this number is huge. Especially when you consider that your business has the potential to maximise CLV simply by optimising your existing email data with a more targeted retention marketing strategy.
But how do you do it without Optimove?
Many email marketing applications provide re-engagement tools for reactivating users before they become inactive. Efficient email marketers also use autoresponders and triggers to manage their email list churn rates.
Granted that educated projections can be made of customer value by studying email sending stats. But collecting data and studying conversion rates is far from efficient.
So how do we substitute guess work, for science?
How do we organise customer behaviour patterns into a strategy for delivering more effective engagement?
And, equally important – how do we manage the churn in a labour efficient way to increase profits and conversions?
The Benefits of Optimove
With Optimove the user can tailor email to increase conversion rates by creating and sending emails to a specific group with particular needs.
In short – Email marketers now have the ability to provide more personalized experiences through relevant, insight-driven, customer-focused and healthy email communication.
Using RFM (Recency, Frequency, Monetary) the Optimove algorithm can discover customer personas and patterns.
Applying this type of data mining to find meaningful customer segmentation is particularly valuable for retailers and B2C businesses.
Optimove groups customers into personas or Target Groups.
This allows email marketers to identify like minded customers and to make predictions based on their behaviour.
Predictive analysis is extremely powerful when creating marketing campaigns, defining lead scores and prioritising which accounts to pursue.
Understanding how customer behaviour evolves can allow marketers to respond more efficiently to the customer’s needs.
Customer behaviour is constantly changing which makes managing the churn rate so difficult.
With Optimove the marketer can understand and predict customer evolution. They will then have the power to improve customer retention by better engaging customers, influencing behaviour and increasing CLV.
Customer Lifetime Value
Optimove calculates and compares customer personas, segments and micro segments. From there it can forecast the financial impact of increases in CLV.
By knowing CLV a business can calculate exactly how much a customer is worth and what should be spent on acquiring more or keeping existing customers.
Whilst businesses can already track customer profit metrics their findings are based on past history. With CLV performance evaluation is forward looking.
The best way to optimise email marketing campaigns is by testing.
Optimove allows for split testing by population customers into A,B and C sets.
That way variables across tested campaigns can be highlighted which can mean the difference between an underperforming campaign and one that brings an outstanding conversion rate.
Setting Up an Email Campaign With Optimove
The user can set up Email campaigns on their own individual schedule (once, daily, weekly or monthly).
Standard campaign setup involves the creation of a control or target group. This is where the user can specify parameters based on CLV.
For example, a target group of users who have spent greater than $100 but less than $500, would be a good segment for a mid level price range product.
An additional parameter for the group could then be within the last 6 months.
The user can place limits on how many messages each customer receives by way of an exclusion period. Any additional campaigns are set to respect or ignore this period to avoid bombarding a customer with emails.
Each campaign triggers a single action which is directed across multiple channels. These can include email, banner ads, facebook, SMS, message boards, call centers, among others.
The Optimove Interface
Optimove has an unorthodox campaign interface. But then as a client retention measurement device it offers a fairly unorthodox service.
Setting up a success measure or multi-split of parameters as explained in the previous section enables a campaign calendar. The calendar is a powerful tool for displaying the incremental value provided by each marketing campaign.
The user can then easily identify the most productive campaigns by viewing the campaign history.
Optimove can predict how a given set of customers will behave in the future based on their interactions.
The analysis is presented clearly and intuitively.
With such a wide array of insights into optimising customer lifetime cycles setting up personalised marketing campaigns is simplified and more efficient in terms of its relevancy to the target market.
The success targets that Optimove supports make it stand above other systems that offer them as an option if at all.
While one would expect this type of analysis to be present in most customer retention programs, it rarely, if ever, is.
Whilst the set up requires some familiarising it is actually intuitive enough to grasp quickly.
There are some limitations.
It lacks a more standard method for reporting that can highlight openers from non-openers. Such a simple device enables the marketer to refine segments and understand what drives results.
Moreover, whilst making customer forecasts it does not suggest appropriate courses of action to take.
That said, both these features are in development for future Optimove releases.
To find out more about Optimove why not request a demo?