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What is data-driven marketing?

by DJ Team, on March 3, 2020

 

 

Data-driven marketing is a method of marketing that uses data, acquired through customer interactions, and from third parties to gain a better view into customers’ motivations, preferences and behaviors. Data-driven marketing ultimately helps companies optimize their marketing channel performance, and in-turn enhance their customer experience, which leads to greater revenue and profits.

What is data-driven marketing?

Why Is Data-Driven Marketing Important?

There’s no question that people have preferences that guide their thinking and behavior. A company that caters to those preferences has a far better chance of converting prospects into paying customers.

For example, a lawn-mowing company sends postcards seeking new customers to an entire metro area but gets less than 1% response. Fewer than 5% of those become new customers. Deciding that data-driven marketing might improve those results, the company studies demographics and discovers a regional area where homeowner income falls 10% to 30% below the median income for the overall metro area. Another postcard campaign to that region that stresses low prices for lawn service produces six times the number of new customers. That second campaign used data-driven marketing to focus on what homeowners in that region found most important.

Using such a data-driven marketing approach can give companies a snapshot of their customers’ buying habits. With that understanding, businesses can tailor their sales and marketing strategies to those customers. Data-driven marketing data sources can include basic personal information such as age, income level, marital status, number and ages of children, birthdays and other demographics.

How Do Marketers Use Data to Identify Goals?

In general, the high-level goal of marketing is to create customers and to deliver products and services at a profit. However, every marketing campaign needs to have a goal. It might be a revenue target, a count of units sold, a count of new customers acquired, and so on. Failing to set a goal for each campaign leaves doubt as to what the campaign actually accomplished.

Marketers can use data-driven marketing insights to establish goals by tapping into various data sources. In the B2C marketplace, for example, these can include demographics, psychographics, big data from social media, CES (customer effort scores), NPS® (Net Promoter Scores), and others. Studying such data can reveal segments of an overall population that have similar preferences, behaviors or inclinations. Understanding those helps marketers refine their messaging and their offers to most closely fit such segments. That process clarifies the establishment of goals.

For instance, you might set a campaign goal to find three key explanations that cause customers to hold negative attitudes toward the company or its products. You might choose to contact a target audience consisting of people who completed a Net Promoter Score survey as “detractors” or “passives” — that is, from those who were not favorably disposed toward the company or its products. Having such data on customers allows marketers to define campaigns they otherwise would have no way of implementing.

How Is the Data Used?

Savvy marketers using data-driven marketing have found many ways to use the data that underlies their marketing efforts. A few examples… 

  • If your running marketing campaigns in a variety of channels, data can be used to identify which channel is performing best at which stage in the funnel with marketing attribution solutions.
  • The Weather Channel sells ad space to companies by analyzing the geographic location of 3 million website visitors. Companies can buy ad space for targeted audiences. For example, shampoo companies that sell “anti-frizz” formulations can directly target prospects in humid climates.
  • Netflix and its streaming video competitors, as well as companies like Spotify, suggest movies and music based on recommendation engines that analyze past customer preferences.
  • Walmart wanted to better understand what consumers are trying to find when they search the Walmart website. Search inquiries are composed of data. Using AI and machine learning, the company analyzed that data to present products that are most likely being sought. It’s led to a 10% to 15% increase in conversion rate.
  • Because automobiles are increasingly connected to the cloud, they send large volumes of data back to auto manufacturers every day. Using behavioral prediction technology, that data can reveal personal experiences of car owners. Therefore, dealerships mining that data can offer customers unique, personalized experiences and can also predict with great accuracy when one is likely to buy a new car. 

Implementing a Data-Driven Marketing Strategy

Marketing experts largely agree on the steps needed to implement a successful data-driven marketing strategy. The topics below represent the steps those leaders and marketing analysts consider important.

  1. Identify the goals you wish to achieve with a data-driven marketing strategy. Many recommend using the S.M.A.R.T. method where your goals are:

    Specific — Rather than “increase” revenue, use a specific target: “increase revenue by 12%.”
    Measurable — They must be reducible to a number
    Achievable — Goals must be attainable. Otherwise, they serve no purpose.
    Relevant — Meeting the goal must benefit the company in some fashion.
    Timely — Set a reasonable deadline for meeting every goal.

  2. Decide what kind of goals you want to establish. These could refer to attracting new customers, revenue, profits, enhancing customer experience and a combination of these and others.
  3. Build a team that will have the skills necessary to analyze the data you collect. It should incorporate people from various departments — sales, IT, marketing, and customer service, for example — to build a cross-disciplinary team.
  4. Build your buyers’ personas.
  5. Decide what data you need. Depending on the goals for a given campaign, you may want to look at the time visitors spend on a web page, their browsing data, social media interactions, data captured by CRM, survey results and more.
  6. Automate your workflow. The amount of data available exceeds the ability of most teams to process and gain insightful perspectives. Choose the automation tools that work with the kind of data you collect.
  7. Collect your data, whether it’s coming in real-time, from a third-party data broker or another source.
  8. Then, use the automation tools you’ve selected to analyze the data.
  9. From that analysis, choose the channels you’ll use to run your campaign. You might choose PPC ads, email marketing, content marketing or any method compatible with your needs.
  10. Finally, launch your campaign. Monitor the results, calculate your ROI, then take what you’ve learned to improve the next iteration.

Data-Driven Marketing Trends

Marketers who wish to push their data-driven marketing further in this new decade will find several trends to investigate.

  • Chief among these is the growing impact of artificial intelligence and machine learning across many industries and use cases. As they pertain to data-driven marketing, those technologies enable companies to get more value from predictive analytics and to interact with customers with greater efficiency, as with intelligent chatbots.
  • Predictive analytics, according to a Forrester report, helps marketers in three ways. It can …
    • Prioritize prospects, suspects and accounts on their likelihood to take action.
    • Help identify and acquire prospects similar to existing customers.
    • Provide more personalized messaging to prospects and customers.
  • First-party data — that produced by the company itself, rather than data purchased from list brokers — is moving to the forefront of data-driven marketing. When customers interact with a given company, the data they produce offers direct evidence that can reveal preferences, behaviors and motivations with more relevance than third-party data.
  • Companies are recognizing that the wall separating the marketing team, the people who create media that powers a campaign, and the data analysts and data scientists must come down. Working together gives the combined team the resources they need to understand how various actions — a click, initiating a chat, downloading a PDF — relates to the customer journey. Collaboration results in more fully informed relationships with customers

Conclusion

Essentially, data-driven marketing expands upon the 25-year-old marketing philosophy first voiced by Don Peppers and Martha Rogers; their then-revolutionary personalized, “one-to-one” marketing philosophy. Focusing on each prospect and customer using data-driven marketing has elevated the one-to-one concept to new heights that had to wait for technology to advance. As tools that leverage AI to combine and analyze various sources of data, like DemandJump, become more widely adopted, you can expect data-driven marketing to become the norm across all industries.

 

Topics:Marketing Analytics
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