Data is your most valuable business asset, but only if you act on it. Unfortunately, a lot of data lies buried in siloed systems that only technical users can access (and often with great difficulty). According to research by IDC and Seagate, enterprises don’t use 68% of the data they gather.
To remedy this, businesses need to build the right data infrastructure to make data accessible and actionable across teams. Let’s get into the how-to behind this strategy.
What is data activation?
Data activation is the process of using data to inform business decisions and activities. You put data into action by making it accessible on business applications. One example of this is having real-time e-commerce data accessible in an email marketing software (to send personalized messages based on customers’ latest purchases).
Activation is the last phase of data orchestration – taking data from multiple locations, organizing and cleaning it, and making it accessible to the tools that need it. Think of software you use across your business, such as:
Customer service chats
SMS marketing tools
You can use reverse ETL tools or custom-built integrations to help push the data you collect to these downstream tools. But it’s important to recognize the time and effort that these two options require. That’s why companies may gravitate to a customer data platform (CDP) which helps automate this transfer of data in real time to offload some of the manual labor expected of engineering teams.
3 obstacles that prevent data activation
Companies that fail to activate most of their data tend to face the following obstacles.
1. Fragmented data infrastructure
Fragmentation occurs when data is scattered across different, siloed storage systems. Information may be spread across spreadsheets, in a data lake or data warehouse, and in the software where it originated.
Because data isn't centralized, it's challenging to draw cross-functional connections and insights. For example, payment data would go to finance and accounting teams, and marketing teams couldn't easily access that information to identify the highest-value customers.
2. Inability to tie events to specific customers
Behavioral data is more valuable when you know which customer performs what actions. Say you’re sending a next-purchase recommendation to customers who bought luggage from your store in the last seven days. You might recommend that they buy a carry-on bag next.
But if you learn that one person on your email list called customer service yesterday to complain about a broken handle, you’d want to hold off on sending him that cross-selling message. You’d first check with the customer success team to see if they've resolved the issue. You’d then send the customer a personalized email to prevent him from churning.
Without the ability to tie events to specific customers, you can’t run personalized experiences. You may even send irrelevant messages that irk customers – and in the process, waste your marketing resources.
3. Unreliable data
It’s risky to make decisions based on data when you can’t be sure of its accuracy and quality.
Say your data indicates that an e-book is your most effective lead-generation tool. But your data set contains duplicates – one person downloaded the e-book five times because they didn’t get an email sharing the download link. And if you didn’t require a work email address for the download form, or your data platform doesn’t have identity resolution capabilities, you might fail to notice that your competitors' employees did 40% of downloads.
The 4 layers of successful data activation
There are different layers to successful data activation – from ensuring accuracy to creating nuanced audience segments. We outline the four different aspects of data activation below.
Layer 1: Accurate data you can trust
Identify the data events you need, the channels where they’re generated, and how you will collect them. Lay out your data collection strategy in a tracking plan. This step will help ensure that you obtain complete data and don't collect unnecessary information.
Establish quality controls and automate data cleaning processes. Possible automations include:
Reformatting data based on your naming conventions
Flagging data that doesn't conform to your tracking plan
Restricting form fields to accept specific types of input (e.g., requiring numbers in a numeric field)
Implementing privacy guardrails, such as removing personally identifiable information
Layer 2: Identity resolution
Build customer identity graphs to spot the same customer across multiple data sources. With these graphs, you can map the customer's interactions with your business to visualize their customer journey. You can create in-depth profiles that teams across your business can use. The more the customer interacts with you, the more data they generate, and the more detailed their profiles become.
CDPs like Twilio Segment come with attribution technology that stitches together customer interactions to form a single customer view. (Learn how identity resolution works here.)
Layer 3: Customer segmentation
Use information from your in-depth customer profiles to build customer segments – groups of customers that share common characteristics and behaviors. Select the most appropriate segmentation methods for your marketing goals and business model.
For instance, to define an advertising audience with a high likelihood of conversion, segment customers based on metrics like lifetime value. Identify the shared traits of these customers and use that information to create an audience profile.
Customer segments also form the foundation of marketing automation workflows. Say you want to show ads on Instagram and Facebook to customers who have abandoned online shopping carts in the last 24 hours. You’d create a customer segment based on that criteria. The automation is triggered whenever a customer gets added to that segment, and that customer starts seeing your ads.
Layer 4: Personalized omnichannel campaigns
Push segmentation data to marketing software to build campaigns based on segments. Without data silos, you can design personalized campaigns across multiple marketing channels. Here’s an example of what you can do:
This automated omnichannel workflow aims to re-engage customers who haven’t interacted with your business recently (e.g., customers who haven’t logged in in the past 30 days or bought a product in the past 180 days). It optimizes ad spend by sending different messages to customers based on their lifetime value and persona. It uses a combination of push notifications and Facebook ads to communicate with customers.
Thanks to API integrations, you can orchestrate omnichannel experiences like this from a single platform. The example above was designed and orchestrated using the Journeys feature of Twilio Engage.
How to activate your data with Segment
Segment’s CDP centralizes customer data, cleans it, and sends it to hundreds of business tools with a single API. Below are the high-level steps you need to take to activate your data with Segment:
Centralize data ingestion with a CDP — Enable customer data integration by connecting your website, mobile SDK, server library, and cloud-based apps to Segment as upstream data sources.
Standardize how you define events — Add standard events to your tracking plan based on the customer actions and conversion touchpoints you want to track.
Tie all events across different tools & channels to a single customer profile — Let Segment reconcile a customer’s behavior across multiple channels, including events when a customer was logged out and assigned an anonymous ID.
Segment your customers based on different attributes — Choose relevant segmentation models and build segments based on in-depth customer profiles and real-time customer data.
Craft personalized customer experiences at scale — Send the right message to the right person at the right time and on the right channel. Scale these experiences by automating personalized omnichannel campaigns using Twilio Engage.