The biggest challenge for marketers is managing customer data. The best solution to this problem is a customer data platform. The added value of this technology is a 360-degree view of all customer interactions on one common data platform to execute all your marketing strategies. Problems like integrating, labeling, and storing customer data can be solved with this technology.
What Is the Link Between Customer Data and Digital Marketing?
Today, companies use different types of data like transactional, profile, product, behavioral, and demographic data that involves impressions and purchases made by specific clients with their company.
Customer data is collected through social channels and web browsers. Data will be stored on preferences like tech, fashion, beauty, gaming, etc. is updated on regular basis, and stored.
Later, this data can be analyzed and used to implement in your marketing campaigns to get higher sales and conversions.
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What Does a Customer Data Platform Do?
Customer Data Platform (CDP) is a software that aggregates data from different tools to create a persistent, and single database of customers containing a variety of touchpoints, and marketing efforts. This data is obtainable to other systems for customer service, customer experience, and marketing strategies.
The goal of a CDP is to enable customer modeling, and optimize the timing and targeting of offers, and messaging.
According to the global survey of Forbes Insights and Treasure Data, 44% of the respondents say that CDP is responsible for driving loyalty and increasing ROI in the organization.
It goes through 4 different components like data collection, data sorting, data storage, and data analysis to increase customer retention, and loyalty. This, helps you understand customer behavior, and make smarter decisions related to businesses.
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How to Use Customer Data for Marketing?
- Journey Map: Track the customer journey across various devices.
- Google Analytics: Ensure that you have integrated Google Analytics to fix loading issues, clean up inactive links, and pages.
- Opinions of Customer: Customers listen to their families and friends. So, in order to gather their trust, you have to leave ratings, reviews, and comments. Also, monitor user opinions on different social media channels.
- Monitor Add-to-Cart Abandonment Rate: User experience is measured by this metric. Minimize the abandonment rate by implementing good quality UX. Also, keep on improving your website so that customers shouldn’t have a bad experience with it.
Following are the scenarios, where you can use a customer data platform:
- Customer Personalization
- Marketing Programs
- Lead Scoring
- A/B Testing
- Customer Segmentation
- Re-marketing, and Re-targeting
- Enhanced Customer Lifetime Value
- Data Collection
- Product Recommendations
- Omnichannel Automation
Listed below are the top mistakes brands make when buying a Customer Data Platform:
- Failing to Set Up Use Cases
- Ignoring about Data Ingestion
- Not Focusing on Audience’s Segmentation Needs
- Ignoring Non-Marketing Teams
- Failing to Prioritize Business Self-Service
What Are the Different Types of CDP?
CDPs are of 5 types based on their functionality:
- Data CDPs
- Analytics CDPs
- Marketing Campaign CDPs
- Delivery CDPs
- Marketing Campaign CDPs
What Problems Does a CDP Solve?
Let’s discuss different problems solved by CDP:
- Siloed Data and the absence of a unified customer view: Siloed data is a problem that enterprises have to deal with. Today, customers look for omnichannel brand experience and so the data collected across all touchpoints have to be linked to one another. Thus, in order to match customer expectations, you need unified data for a unified customer experience. A CDP collects customer data from various offline and online sources such as CRM, ERP, mobile, social media, website, and e-commerce sites, and eliminates silos by integrating all data.
- Incomplete Identities: Both privacy management and identity are considered to be of major capabilities. Customer ID of numerous data sources isn’t mapped to each other to create a strong, and perfect customer profile. This is where a CDP can help.
- Disintegrated segmentation: Data scientists say that there are no algorithms created for beating bad or missing data. Incomplete, and lagging customer profiles result in poor predictive models, and analytics. A CDP can be used to segmentize customers into various baskets.
- Generic campaigns without value: Sending generic campaigns to a wide range of audiences is an ineffective approach. Businesses need to send relevant, value-added, and tailored promotions at a specific time, and channel. A CDP uses customers’ cross-channel data and takes the help of machine learning algorithms to create complex micro-segments. This leads to personalized marketing, which is necessary to enhance customer lifetime value and reduce customer churn.
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Addressing CDP Pain Points and Pitfalls
While a robust CDP solves many marketing challenges, implementing any new system comes with a few pain points. There are certain mistakes both B2B and B2C brands make when purchasing a CDP.
Here are a few CDP related pitfalls every business should avoid:
A big bang phase:
Marketers think that CDPs are powerful weapons that solve a company’s problems within a short period of time, but the fact is it requires time. It’s better to avoid integration of all probable sources, processing, and activation steps into a single release. It is advised to gradually add end-to-end flows, maintain, monitor, and move to the upcoming one.
Irregular event models:
Irregular event models across different data collection channels. Functionally similar, and data representing business events should be similar.
Ignoring “fast” wins:
With the help of CDP, you can focus on building a strong strategy around customer behavior, and provide personalized experiences so that customers like to connect with you for a business. Concentrating on these points sometimes leads to ignoring important points, which results in success.
There are many small considerations like an increase in email open rates for a campaign or an increase in revenue after a transaction done by customers.
Getting event granularity just right:
Too fine granularity results in scaling issues. Similarly, too rough granularity ends with a loss of meaningful business insights.
In the meantime, companies should prepare a perfect strategy while releasing a CDP. It requires continuous efforts from your company. For this, you have to choose a few employees as core users and prepare workshops, and cheat sheets to launch the CDP.
Inability to grow the customer base:
A CDP is responsible for lookalike marketing, which is the best way to grow the customer base. Retailers use insights provided by CDP to their present high-value customer profiles to find out related possible customers and target such users with acquisition strategies.
The lookalike is the best option for customer acquisition as per gender or age. A CDP has inbuilt analytics models, which helps marketers eliminate spray and pray options to save acquisition costs.
Failing to define use cases:
To avoid this mistake, businesses need to prioritize their use cases based on their customer journey and understand what their business needs.
Ignoring data ingestion:
Look for technologies that enable businesses to ingest data from the source. This will lead to a reduction in data silos and enhances accuracy.
Not focusing on business self-service:
Businesses need to prioritize self-service to extract customer information and find out new opportunities. This will help in generating revenue.
Ignoring their segmentation requirements:
It is true that CDPs can meet the standards of basic segmentation but some platforms fail to match the standards because of limited data storage. So, the responsibility of the business is to ensure that the CDP can store unlimited data and segment customers from the basic to the pro level.
About the Author: Senior SEO Specialist at Express Analytics. Has experience in SEO, Social Media, Blogging, Online Reputation Management, Google Ads, and YouTube Video Optimization.
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