We’re experiencing neck-and-neck competition in the current state of the digital marketing world. 2019 brings new methods of leveraging data to make smarter business decisions, now available in the form of customer data management software – and as companies are quickly realizing – they produce measurable results. But if those efforts are not well calculated, well thought out, and well aligned with your data management goals and functions, businesses of any size can find themselves drowning in this torrent of information.
As proclaimed by the famous management expert, Peter Drucker:
Essentially: don’t waste effort measuring everything that moves. Instead, significant energy should be spent determining what to measure, and what importance will be placed on the numbers.
So, with data flowing in from every corner, what should be measured? Where should it be stored? How should it be used to make valued business decisions?
And of course, the most difficult part of the process—where to start?
This article will outline how to create a system of customer data management from scratch, including:
- What most companies are doing wrong when it comes to gathering data;
- Why companies still gather data this way;
- What these companies need to do/have to gather data correctly in the future.
So where do you begin? Let’s take a look.
Table of content
Setting the Scene
The key to company success now lies in the ability to triumphantly manage streams of customer data. Though as the Harvard Business Review reports, most companies still remain badly behind the curve:
- “Cross-industry studies have shown that on average, less than half of an organization’s structured data is actively used in making decisions—where less than 1% of its unstructured data is analyzed or used at all.”
- “More than 70% of employees have access to data they should not.”
- “80% of a data analyst’s time is spent on just discovering and preparing data.”
We’re now faced with new challenges; be it cross-department communication, providing competitive customer experiences, or making critical decisions on the fly. Correctly gathering data on customer and users deliver huge benefits to organizations across all sectors – which is why companies turn to new solutions in data aggregation that improve that performance.
Customer Data Management and How it Affects Us
Customer Data Management (CDM) is the process of gathering, organizing and analyzing data about your customers. It’s a crucial mechanism when considering improvements to:
- customer acquisition, satisfaction and retention rates;
- visibility of customers and communication strategies;
- and increased data quality and higher revenue.
That’s quite a repertoire, though once represented with the facts, the overwhelmingly positive results are hard to ignore. Still not convinced? Here’s a breakdown on why creating a reliable customer database should be your top priority:
- Acquiring new customers is no easy task, however a customer database can drive your business communications without you having to spend a fortune on lame, detached advertising.
Collect customer data by simply asking for it and reward that trust by communicating relatable discounts, events and other promotional reminders (e.g. free shipping for first purchases). A pleasant first impression improves the likelihood of having clients become strong brand promoters.
- A whopping 80% of sales comes from 20% of your existing customers. Once they’re in your door, retain them by cultivating a healthy customer loyalty program which creates customized, positive experiences to produce recurring brand advocates that generate highly-valued word-of-mouth marketing.
Only with a fully functioning customer data management strategy can your marketing team start calculating important metrics like customer value over time, also known as the Customer Lifetime Value (CLV).
- Collecting relevant customer data will allow you to better segment your target market, discover trends in buying behavior and allow you to customize individual communication strategies —leading to better-informed, real-time strategic decision-making.
- The customer’s path to purchase can be a long, unpredictable process with multiple touch-points, numerous devices, around the clock consumption, and participation both online and offline. Relevant data collection form every stage of the customer journey can be analyzed to identify performers which can be supported to enhance sales efficiency.
An underlying factor begins to stand out: for a customer data management system to successfully produce results, data collection from various sources need to be coordinated around the customer rather than channel or device, creating a holistic view of individual customers called the Single Customer View (SCV).
As a result, the newly centrally located and widely accessible customer profile is available throughout company departments as opposed to being warehoused in separate locations under lock and key. Each customer interaction is then relevant to their previous one, regardless of the channel, encouraging consistent, transparent experiences with your brand.
Collect the Right Type of Data
Are you collecting the right type of customer data? A truly effective database needs to identify the type of data it’s collecting, as well as its value. We have this data split into four key segments: Identity, Quantitative, Descriptive and Qualitative data samples. Below you’ll find a description of each with examples, along with ideas on how to collect them.
- Identity Data: By collecting the customer’s identity data, we’re able to uniquely profile the individual with the basic information needed to build a foundation, along with whatever contact details we need to reach out to them. Once the buyer persona is comprised, communication can be customized to their specific niche throughout their specific stage in the customer journey.Examples of identity data may include:
- Name: Title, First Name, Last Name, etc.
- Personal: Date of Birth, Region, Gender, etc.
- Address: Shipping Address, Billable Address, etc.
- Telephone: Home No., Work No., Cell Phone No.
- Social Network: Facebook, LinkedIn, Twitter Address, etc.
- Account: User IDs, Payment Preferences, etc.
How to collect Identity Data: Typically, you’re already collecting this sort of data when your customers enter their payment details upon check out, sign up for your newsletter, or voluntarily hand it over in order to receive a product, service, or incentive.
Depending on your industry, you may also consider:
- Tailored sign-up forms
- Discount vouchers for first purchases
- Providing pre-order opportunities
- Tailored eCommerce checkout process
- Warranty cards
- Loyalty/rewards programs
- Quantitative Data Examples: Once you’re done getting to know the customer on an individual level, it’s important to understand how the customer is interacting with your business using measurable operational data, or quantitative data.Sounds familiar? You’re absolutely right. Quantitative data is information collected throughout the customer journey, right down to discovery details, various channel interactions and conversion-specific steps that led to the purchase.Examples of quantitative data may include:
- Online/Offline Transactions: Product Purchased, Amount of Purchases, Time of Purchase, Order/Subscription Value, Order/Renewal Dates, Cart Abandonment, Product Returns, etc.
- Inbound/Outbound Communication: Date, Time, Channel, Opens, Click Through Rates, etc.
- Online Activity: Website Visits, Product Views, Online Registration, etc.
- Social Network: Social Handles, Groups, Interactions, Interests, etc.
- Customer Service: Complaint Details, Customer Query Details, Call Center Communication, etc.
How to collect Quantitative Data: The aim of the quantitative data game is to understand the decision making process of your customers as they interact with your company. What led them to discover your business? Which channel drives the most conversions?
Channel-specific tools are available throughout the customer lifecycle and should be tailored to measuring your marketing goals and strategy.
Where to start collecting quantitative data:
- Web Analytics Tools such as Google Analytics
- Website cookies/mouse-tracking heatmaps on landing pages.
- Tracking pixels in emails/newsletters
- Recording historical purchase transactions
- Recording historical customer support communication
- Social media activities
- Descriptive Data Examples: As a step up from identity data, descriptive data aims to collect additional demographic information that further outlines customer personas. Once clarified, you’re one step closer to using predictive analysis to implement optimal timing within your marketing efforts.Examples of descriptive data include:
- Family: Marital Status, Relationships, Number of Children, etc.
- Lifestyle: Property Type, Car, Pet Ownership, Hobbies, Collections, Interests, etc.
- Education: High School, College, Advance Education, etc.
- Career: Job Title, Job Description, Income, Professional Background, etc.
How to collect Descriptive Data: Obtaining high-quality descriptive data is no easy feat and requires additional ingenuity. Companies typically turn to in-depth questionnaires for their data collection, which dive into discovering seasonal growth and decline, buying behaviors, and lifespan of the customer cycle.
Here are a few methods to collect descriptive data:
- Open-ended interview questions
- In-depth questionnaires and surveys
- Observations of target behavior
- Focus group interviews
- Advanced lead forms
- Qualitative Data Examples: Lastly, we have qualitative data, which should describe the reasoning behind the choices your customers make. Questions will typically start with How, Why and How, including “how opinions and attitudes are formed,” why people behave the way that they do,” and “what are the differences between social groups.”Examples of Qualitative Data include:
- Attitudinal: Perceived Value, Rating, Feedback, Repurchase Likelihood, etc.
- Motivational: Reason for Purchase, Customer Needs, etc.
- Opinion: Likes/Dislikes, Preferences, etc.
How to collect Qualitative Data: Approaching qualitative data collection can be a bit tricky, since collecting deep insight into customer habits is more time-consuming, thus more expensive than just collecting quantitative data.
Regardless, available methods include direct interaction on a one-to-one basis, direct interaction with individuals in a group setting, or indirect interpretation of customer opinions on various communication channels along the customer journey.
Qualitative Data can be collected in the following manner:
- Industry-related review websites
- Social listening with social media monitoring tools
- Tailored newsletter sign-up process
- Employing a favorite, save or rating system
- Deep listening and satisfactory feedback surveys
These are just a few simplistic data categories to abide by, but are not limited to industry-specific data that might be unique to your business case.
As some may work better than others, a study performed by Ascend2 discovered that the most effective data sources were sales and customer service teams, quickly followed by marketing programs according to 50% and 45% of marketing influencers.
In other words, having your own customer data management platform – explicitly one that combines – is the most important source of marketing data for your company.
Choose the Right Tool for the Job
In the beginning, manually storing data on Excel sheets or similar spreadsheet software may seem like a low-cost, reasonable solution. But as your business grows, so does your data, and you might find yourself lost in the complex, voluminous data sets offered by today’s big data trend — not to mention a lack of valuable insights that could give you a competitive advantage.
However, that’s not even the biggest threat to your revenues. Overlooking initial customers and their Customer Lifetime Value (CLV) when overly focused on acquiring new ones is the real issue.
Sooner than later you’ll need efficient software to store, track and make sense of all the incoming information. Though it’s just as wise to invest in the right tools from the start, rather than having to adjust once you run into lost customers, poor reputations and the limitations of a spreadsheet. There’s a wide variety of customer data management software available to store customer data, but in terms of providing the personalization at scale that many consumers now expect, there’s one that stands out.
CRMs vs DMPs vs CDPs
Historically speaking, Customer Relationship Management (CRM) platforms were the first data aggregation platforms to come out in the 1990s. As a customer data management system, their soul purpose was to collect known details (first-party data) about customer – think Identity and Quantitative data rather than Descriptive and Qualitative data – and managed these interactions.
- CRM platforms are built to engage with existing customers, gathering strategic information to fuel improved customer service, aid sales initiatives and better informed marketing agendas. It’s a start to having your data well organized and accessible among various departments to monitor customer interactions with your company.
- A CRM can quickly automize many marketing tasks, including lead creation from sign up forms and quick reporting. With an improved time management there’s space to focus on more important details.
- Easily integrate a CRM with external tools to customize your and gather even more data.
- CRMs were never built to know about visitors before they become customers, making the Single Customer View a difficult achievement – and an admittingly expensive endeavor – when adding social media channels, advertising campaigns, web behavioral data, and other sources of data into the mix.
- Where CRMs lack most are real-time capability, since they’re great at managing tens of actions, thousands or even millions of times – but they aren’t that adaptable. The more you customize them, the more messy and unmanageable they become.
- DMPs allow you to stitch bits and pieces of second- and third-party data from cookies, email addresses and other behavioral data to divide users into segments. These segments can then quickly be put to use when personalizing media and dynamic advertising.
- DMPs only store anonymous, third-party data with limited segmentation, so they’re heavily outperformed when dealing with more precise identity matching. Thus, creating a unified customer view with multi-department sharing is completely out of the question.
- Data from DMPs are short-lived, based on the 90 day lifespan of a cookie, and that’s not to mention how less effective cookies are after GDPR legislation has taken effect.
- Integrating a DMP into your organization’s current environment might be challenging. It requires significant technical and domain knowledge. Such data aggregation technology might turn out to be too complex for employees, introducing a steep learning curve associated with learning how to properly use it.
As the Big Data trend became abundant, so did the troves of data, creating a dire need for flexibility and scale that could take on the demand for improved customer experience and omni-channel marketing initiatives. As the newest player among the aggregated data platforms, the Customer Data Platform (CDP) easily integrates with existing data, incorporating first-, second-, and third-party data, as well as offline and unstructured data, all in one system.
- While CRMs and DMPs provide segmentation, CDPs centralizes all the customer data coming into your company, regardless of what channel or device the customer used. It organizes all the data you collect around the customer, rather than around the channel or device it was gathered from. The more data sources you have for the CDP to pull from, the more powerful it can be. A CDP is the beating heart of customer data that makes a Single Customer View possible.
- CDPs support real-time data streaming to take immediate action throughout the customer journey, such as personalized recommendations, activating audiences and frequented cross- and up-selling opportunities. Flexibility at this scale can be applied to a wide range of channels and purposes, creating a more actionable marketing initiative.
- Built for Marketers – not IT Developers. The all-in-one nature of CDPs make it very easily integrated into the existing company environment, without the hassle of creating custom integrations on multiple modules. Having multiple data sources unified into a single source of customer information enables quick cross-department communication providing a connected customer experience across all channels using the same interface.
- CDPs are driven by first-party data, meaning they almost exclusively live off of the data collected by your marketing initiatives. Though it’s more personalized and based off of real client information, this all has to be collected, and you might find yourself lacking the quantity of data needed to run marketing initiatives at full steam.
- Contrast to DMPs, issues with limited data sets further include the inability to acquire external data sources, such as second- and third-party data sets, to incorporate in marketing activities.
Here’s a quick tally of our results:
As the table depicts, the most time- and cost-efficient way to manage customer information, is to use an all-in-one Customer Data Platform.
An extensive customer database cannot be built overnight – it takes some time and effort from the business owner to gather the right type of data, find the right method of storage, and apply the necessary safeguards for correct use. If done correctly now, over time this information will become an asset to your business, helping you grow and succeed in your endeavors – if ignored, it could come back to bite.
It’s now considered commonplace for customers to expect a personalized customer service. So why not provide a consistent cross-channel customer experience with appropriate recommendations, and tailored communications? It’s much easier to retain existing customers, than to fight to get them back.
This is why it’s so crucial to have a well-maintained, accessible and insightful Customer Data Management system – and now, a good Customer Data Platform can make that possible.
Exponea is a customer experience and customer data platform that not only boosts e‑commerce growth with AI powered engagement automation, but also helps improve our clients’ culture with better cross‑department collaboration and customer centricity.