Data Segmentation: Way to Add Value to Your Audience Data

Data segmentation is a need of the hour for marketers to stop guessing and close more deals.

The entry of social media into our lives has changed the game; it created a sense of urgency in companies to understand the customers and offer the right product/service without any miss.

One-to-one marketing is the possible way to attain it.

Yeah, it’s possible to target your audience on an individual basis, all it happens using data segmentation techniques.

Want to know how? Stay tuned with this article. Here we have jotted down a list of what we are covering-

What is Data Segmentation?
Types of Data Segmentation
Why is Data Segmentation Important?
Key Benefits Of Data Segmentation
Challenges To Effective Data Segmentation
5 Ways to Improve your Data Segmentation
How Can You Perform Segmentation of Data?
Let’s start!

What is Data segmentation?

Data segmentation is the process of making your data to be used effectively for marketing and operation purposes.

The data that you collect is in bulk and unstructured, to make it usable, you need to apply proper audience data segmentation techniques.

That involves all your collected data being organized and divided into defined groups of people with similar interest or characteristics.

Thus, it will be easy for your marketing team to get clear, concise and actionable information to target their intended audience.

Audience Data segmentation is not constrained only to targeting; it can also be used for gaining real insights into your audience, customer satisfaction, improving ROI, and many more.

Let’s Know, What are the Various Types of Data Segmentation
You can perform segmentation in multiple ways, but strategic data segmentation can build a higher level of a success rate than simply generating a group with similar characteristics.

Simple grouping doesn’t involve algorithms, scoring, or real-time event-based information.

Whereas strategic grouping focuses on behaviour, or customer interests, preferences and many more.

It mainly involves profiling, predictive modelling, customer state vector, event-triggers and real-time decisioning.

1. Customer Profiling
Customer profiling mainly involves- Demographics and behavioural profiling.

Demographics data is simply the general data you collect from your audience such as- age, gender, first and last name, location, salary, credit score, etc.;

Behavioral views include measures such as-

Preferred communication channels.
Online characteristics (online engagement with blogs, forums and social media platforms)
For example,
Some people spend most of their time on Instagram for news and online shopping, and some prefer Facebook to see long videos instead of short videos on Instagram for the same purpose.

Profiling can be performed in two ways- analytical and operational ways.

Analytical Profiling
It’s based on customer activities that are dynamic in nature along with demographic data.

Operational Profiling
It’s like an instruction manual that describes what could be the possible steps you can take with the particular profile in the future. It covers all touchpoints of the customers along with their analytical profile.

2. Predictive Modeling
It involves connecting different layers of data-points that are connected in one or the other way.

In this, the variables that are responsible for a change in outcome are collected first, such as- next best offers, security management and financial predictions.

This data is used to predict the future purchases of the customer.

For example- If someone is looking for shoes, you can check with the available offers on shoes and reach them via their most preferred channel.

3. Customer State Vector
It builds a single view of the customer in a centralised location on a real-time basis gathered from various departments and divisions of the organisation.

Let’s understand this with a simple example below in this diagram-

4. Event-Driven Marketing
It mainly involves triggering the transition from one behaviour to another. Types of transition mainly include:

And Latent
Characteristics of Blatant Events:
One-time transactional events or anomalies.
Specific events of life
Events that are easy to discover by many transactional systems and detection engines.
Characteristics of Latent Events:
Trend based events
Events that need a more complex detection engine.
Forward-or-backwards looking events.
Event triggers cover most of the actions of the customer, product, marketing, account, risk and fraud. Here are some examples-

Customer birthday, retirement, and anniversary
Address or marital status change
New customer
Birth of a child and many more.
5. Real-Time Decisioning
It’s similar to customer state vector, but it permits for more coordinated and streamlined communications across various channels in real-time.

Hope You’re Cleared with the Types of Data Segmentation, But Why is Data Segmentation Important?
Audience data segmentation becomes a de rigueur part of any company to target better its audience.

Look at this stats for better understanding-

Almost 30% of marketers surveyed practice audience segmentation strategies to enhance email engagement. (Hubspot, 2020).
Email campaigns segmented by user preference see 74.53% more clicks than no-segmented campaigns. (Mailchimp, 2017)
Targeting is not the only one which can attract you towards data segmentation, here are more reasons to put light on it-

You can create messaging that is customized to a specific set of audience that is very relevant to them at the current point of time.
It permits you to get better insights into your customers.
Segmentation of data allows you to analyze better the data you hold in your database, that can help you to plan future steps by identifying the opportunities and challenges in it.
It enables you to mass-personalise your marketing campaigns, that reduces costs.
Improves customer satisfaction
Better ROI by targeting right at the right time with the right offers to the right person.
Now, Let’s Know, Key Benefits Of Data Segmentation
When you start seeing the benefits of data segmentation, you can get a long list of it that are directly or indirectly related to it based on the database you have.

Let’s put a light on some common benefits of it-

1. Lead Generation
Audience data segmentation is essential for your sales and marketing team, to plan and organise their approach pipeline based on priorities (whom to approach first, next and so on).

When the sales team knows what your customer is looking for and what is their interest, it can save their lots of time and effort.

And also keep your sales and marketing team focussed and goal-oriented.

2. Enhance Cold Outreach Success Rate
When your sales team has all the details of your prospects and customers, it will be effortless for your team to approach the right person with the offers they are looking for.

It saves time and efforts of your sales team and leads to more conversions.

3. Improves Customer Engagement & Brand Loyalty
Today’s customers are more inclined towards brands which can give them a customised service or make them feel special.

Audience data segmentation can help you in doing so, by segmenting the audience based on their preference and interests.

The segmented data can be used to target marketing and ad campaigns to fulfil the needs of the customer. Moreover, you can use it for audience remarketing across the entire digital ecosystem.

Satisfied and engaging customers is the magical mantra for the brands to enhance their brand loyalty and retention rate of their customers.

4. Data-Driven Marketing
Data driven customer segmentation makes the brands to utilise their database more effectively, by using their data in various ways such as for-

Support for the sales team,
Data exchange with partner brands,
Selling your data,
And similar other purposes that can add value to your data moreover increase your additional income from data.
5. Optimises Cost-Efficiency & Resource Management
Audience data segmentation helps the brands to better utilise their resources based on the actions of the customers.

It provides more in-depth insights and helps the brands to recognise the user segments who have a high probability of conversion or leads to more profits and enables them to carry out more focussed marketing.

Challenges To Effective Data Segmentation
After getting an understanding of how audience data segmentation is crucial for your company and its benefits, you might be excited to practice the same in your company. However, to achieve this, you need to tackle some challenges.

Let’s discuss here the challenges and probable solution for it.

According to the research of Experian, 94% of companies find difficulty in audience data segmentation.

The Top Challenges They Encountered are-
Gaining insight quickly enough
Having enough data

Let’s discuss each in detail-

1. Gaining Insight Quickly Enough
Segmenting data is the primary task; it’s easy if you have moderate data. But what in case of a massive amount of data. It can create a delay in getting the right segmented data to the sales team.

To tackle this, you must be clear with this questions-why are you targeting? And who will be going to your targeted audience?.

Once you’re clear with this answer, you can easily focus on those user segments and use it immediately.

With this, your sales team can have accurate and segmented lists of people to contact. And the marketing team can create campaigns and refine their targeting to rely on real insights.

2. Having Enough Data
How can you ensure you have enough data for segmentation?

Most of the companies end up having inaccurate or incomplete data. For example- some have mobile numbers and some with mail IDs.

You can take either mail id or mobile number as a common identifier, but the issue here is people use multiple mail ids and numbers that lead to duplication of data.

How can you tackle this? Simply by data enriching and getting a unified view of the customer.

Audienceplay data segmentation is such a platform that can help not only to enrich your data by adding all the missing attributes of your audience data (using the data from their other partner brands).

But also segment it on a granular level precision and get a single view of the customer. More importantly, it can help you in improving your revenue by making your segmented data groups available to the other brands for marketing purposes.

Here are some other challenges with data segmentation-

5 Ways to Improve your Data Segmentation

1. Enrich Your Data to Qualify Your Audience
Create a single customer view by collecting all the information about the particular audience in one place. In a single view of the customer, you can cover all touchpoints of your audience with your brand. For example-Their-

Demographic details,
Geographical details,
Mode of purchase,
When they purchase the item last time,
When the item going to expire,
How often they are visiting your site,
Which product or page they are visiting the most,
What is the frequency of their visit to your website? And many other precise levels of information.
2. Integrate an Omnichannel Strategy
In traditional days it will be easy for the marketers to target their audience by mass marketing because they make use of a single device or platform.

But now people are making use of multiple devices(Mobile, Laptop, PC and tablets) and platforms before purchasing a product.

Let me explain to you with an example- A person sees the product on the website, gets an offer message on the phone, switches to another vendor and then buys it from an offline store.

So from this example, you can’t predict the behaviour of the customer in most of the cases. The stages of the customer journey from planning to purchase a product until they buy a product switch from one platform to another platform.

In this situation, if you’re sticking to one platform will lead your customer getting the wrong messages, for example- if someone already purchased a product and again you’re approaching them that’s going to be waste.

To overcome these, brands are adopting the omnichannel approach to track customers wherever they move on any platform or use any number of devices.

3. Identify the Right Segmentation Criteria
You can’t apply the same segmentation of data criteria for every business; the customer data segmentation criteria are different for different business for example based on-

B2B and B2C
Business objectives
Buyers profile
Before your user segments, you must clear with the purpose of segmentation of data and whom to target by considering their sociodemographic, psychosociological and behavioural data.

4. Real-Time Segmentation of Data and Personalisation
Using automated data segmentation tools that can track the real-time actions of the customers, you can customise the messages and offers to your audience.

Real-time segmentation of data joined with automation scenarios, and predictive algorithms can help you to reach your audience at the right time with the right offers.

That, in turn, ends up with customer satisfaction and better ROI.

5. Prioritise your User Segments Based on Their Value
Segmentation of data based on their state of the customer journey, their interest, the urgency of purchase can help the brands to reach end goals easily.

Handling every customer, in the same way, will result in missing leads.

Prioritising your customer based on their state is the most crucial thing to consider during segmentation of data.

How Can You Perform Segmentation of Data?
Audience data segmentation is not a daunting task when you have a moderate amount of data. The real challenge comes when you have a massive amount of data.

The major task here is to make use of proper technology and software systems to carry out all this data segmentation properly.

And all these you can’t perform on your own without the expert knowledge.

Here comes the Audienceplay-a free DIY platform that can be the most probable solution to your data segmentation issue. By just uploading your data or integrating your SDK with the software system, and then segment customer data based on your defined parameters.

To Sum up
Audience Data Segmentation is the need of the hour for the companies to reach the right audience at the right time.

It helps the brands not only in targeting but in all other ways that can ultimately lead you to reach your end goals.

While implementing audience data segmentation, the major challenges are- Gaining insight quickly enough and having enough data.

You can tackle this, with the right software and technologies- Audienceplay is one such platform that is the complete DIY solution to your data segmentation issues and beyond that.

Want to try? It’s free.