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Why Your Marketing Department Needs a 360 Customer View

Why Your Marketing Department Needs a 360 Customer View

If you’re in the marketing world, chances are you’ve seen or heard of the term, ‘360 customer view,’ making a buzz. Suddenly, enterprise-level organizations are talking of customer personalization, aggregating data to build 360-degree views, and omnichannel interactions.

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While all this sounds great, there is little information on how companies can actually achieve a 360 customer view. Also, how many marketers really understand what the term encompasses and what kind of work they would need to do to attain this goal.

With this piece, I hope to help marketers understand the term better, why it matters, and how you can make a 360 customer view a possibility.

Let’s dive in.

Understanding the challenges with customer data

A simple definition of a 360 customer view is, ‘aggregated data that can give you a complete picture of your customers.

As simple as this sounds in theory, it’s mostly an unattainable objective because of the very nature of data itself.

In a nutshell, customer data is inherently messy. No matter what controls you put in place, your data will always be flawed. The three big troublesome Ds of data that plagues every business is:  

  • Duplicate Data. This is the biggest problem companies have to face. There are literally 101 ways duplicates can be created. A customer entering conflicting information. A data entry operator entering the same information multiple times. A system error that results in data duplication. A CRM migration gone wrong. There is very little you can do to prevent duplicate data from happening.
  • Disparate Data Sources. It’s not uncommon for businesses to use multiple tools and platforms to store and process customer data. Your marketing team may be using HubSpot, while your sales or customer support team may be using Salesforce. Come the time of reporting, you realize the data is messy, differs in structure and format, and is unable to provide the right information.  
  • Dirty Data. Rising data complexities lead to data quality issues. Dirty data refers to data that has format issues, typos, misspelled names, incomplete or duplicate data. Basically, dirty data is data you cannot use for its intended purpose – and that is never a good thing.

Annually, millions of dollars are wasted in return mails, lawsuits, expensive mistakes, inaccurate insights, and analytics – all because companies do not check the quality of their data and don’t bother about it until they have to fulfill a critical goal – like customer personalization.

Here’s a preview of bad data:

Bad Data 360 Customer View

What’s the link between data and customer personalization?

To personalize an experience, you have to find out the hidden relationships between people, products, their environment, their behaviors, their experiences, and their expectations. All this can only be found via data reconciliation.

You must incorporate a customer’s relationship with other entities such as the products they buy, the organizations they are associated with, the brands they follow, their household data, their favorite stores, locations etc. You must get the customer’s complete journey in their day-to-day life to be able to personalize a service for them. This is the crux of a 360-customer view.

Why do you need to do this?

To stay ahead of the curve. And honestly, this is the best time to get started.

By 2020, Gartner predicts that more than 40% of all data analytics projects will relate to an aspect of customer experience. (Gartner)

Companies that invest in customer analytics do way better than their competitors. The reasoning is quite simple – if you know what your audience wants, and can deliver on their expectations, you win them for life. But to make this possible, you will need to get your data in order.

An example of 360 customer view

Let me explain 360 customer view with a real-world example.

A renowned insurance company has dozens of services that are offered in liaison with third-party vendors and partners. In an attempt to create a more personalized experience for their customers, the company decided to sort their data and understand the customer’s journey through various touchpoints and the services they acquired.

The company discovered that vendors and partners used their respective CRM tools and platforms. The format and structure of data differed significantly. To top it off, the company used its ERP system that had a different data collection format and structure than what the vendors used. For example, the company recorded dates as Month/Day/Year. Some vendors recorded the date as Day/Month/Year.

It took them six months just to collect the necessary data from its vendors and partners. But that was not the end of the ordeal. Next up, they had to clean that data, standardize it to meet the required format, and run matches to weed out duplicates. This process took another two months. Finally, after eight months of data cleaning, data matching, and data merging, the company was able to get a consolidated record of its customers and began initiating their customer personalization plans.

Keep in mind, the company opted to refine only one segment of their customer data – that of loyal customers who were associated with more than two services for the past five years. With this information, they intended to create personalized campaigns and services such as loyalty cards, exclusive memberships, and other services to make their customers feel cherished.

See how simple the idea of 360 customer view sounds but how difficult it is to get done? Yet companies are willing to go the extra mile because, in the age of digital, customers expect personalized services from their favorite brands.

So how do you get started?

Obviously, this is a big undertaking. Companies fail because they underestimate the problems of data quality. This is why, if you’re planning to experiment with a 360 customer view, I highly recommend the following:

  1. Start Small. Create a goal, an outcome, and a process that you can manage. For example, testing out loyalty cards to the top 200 active purchasers is a good place to start. Once this is a success, you can then move on to a larger audience segment.
  2. Don’t Aim for Perfection. You can never get a complete view of all your customers. This is why it makes sense to create small goals and aim for what is doable.
  3. Invest in the Right Tools. Nope. You cannot do this using Excel or Google Spreadsheets. If you’re pulling in data from multiple sources (CRMs, transaction data, behavioral data), you’ll need to invest in a tool that allows you to consolidate, dedupe data and clean up dirty data. Lucky for you, there are best-in-class data quality management solutions out there that are great for business users. You don’t need to know data science to solve data issues.
  4. Don’t Wait for ROI to Happen. Insights and clean data are only the start of the journey. You have to turn these insights into activities that drive ROI. Most organizations get insights, but they don’t have a plan for how to use those insights to achieve their goals.
  5. Fix Your Data. I can’t help but reiterate this. Bad data will be a huge bottleneck in EVERY aspect. Regardless of whether you want a 360 customer view or not, you must make data quality a priority. A countless number of businesses have failed (with millions of dollars down the drain) because of bad data. So whether it’s for something as normal as a promotional campaign or for something as critical as annual reporting, you need data you can work with.

The most important question to ask before you begin is, ‘Do I have data I can trust to create a good experience for my audience?’

To conclude

I’ll keep it short. Want your customers to choose you over your competitors? Give them the experience they need. But before you attempt to woo them, fix your data, give meaning to vague terms like ‘data-driven’ or, ‘data-centric,’ and be ahead of the curve.

Guest author: Farah Kim is an ambitious content specialist, known for her human-centric content approach that bridges the gap between businesses and their audience. At Data Ladder, she works as the Product Marketing Specialist, creating high-quality, high-impact content for a niche target audience of technical experts and business executives.

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