Tables of Contents
- What Is Data-Driven Marketing?
- 5 Data-Driven Marketing Strategies
- What Do Marketers Use to Analyze Big Data?
- Data-Driven Marketing Benefits
- How to Use Data-Driven Insights
Ads permeate all media channels, causing people to pay for ad blockers and subscribe to ad-free services. However, consumers still respond favorably to some ads, and marketing and advertising budgets continue to grow, albeit more slowly than in the past.
The key is to know which ads and ad channels will click with the consumers that the marketing campaign is targeting. To achieve this goal, advertisers apply data-driven marketing strategies that reach a campaign’s intended audience with personalized messages that enhance the customer experience.
Market research firm Invesp reports that marketing firms that exceed their revenue goals apply personalization methods 83% of the time. In addition, businesses that use data-driven personalization recorded between five and eight times the return on investment (ROI) on their marketing budgets.
Here’s a look at five ways data-driven marketing is being applied by companies, and the tools and techniques that help marketers reach their desired audience and effectively deliver their message.
What Is Data-Driven Marketing?
Understanding what data-driven marketing is begins by looking at the many ways consumer behavior has changed in recent years and the technologies that make it possible to collect and analyze massive amounts of data about customers, markets, and industries. Data-driven marketing applies the latest data analytics capabilities to pinpoint the most productive media buys and to craft creative, personalized awareness about products.
Technology Ushers in the Age of Personalized Marketing
New marketing and advertising technologies make it possible to personalize every aspect of marketing. Prime examples of personalized marketing are the product recommendations that Amazon customers find each time they log in to the online shopping service.
Digital marketing firm Stirista outlines the steps required to implement a personalized marketing campaign:
- Identify the demographics of the campaign’s target audience, such as women ages 18 to 34 who spend more than $50 a month on beauty products.
- Determine the products and services that generate the most revenue in that target category.
- Use data analytics tools to gain insight into the campaign’s target audience, and convert those insights into personalized experiences for those customers.
- Apply A/B testing and other variations to learn which message and medium delivers the highest level of customer engagement.
Social Media Today describes hyperpersonalized digital marketing as the combination of internet and mobile technologies to harness “all forms of data being used in unison across all marketing channels and customer journey stages.” The goal is to know what the customer wants before the customer does and to move customers from “top of funnel awareness to post-purchase happiness in record time through higher and more effective engagement at every stage.”
Techniques That Enhance the Customer Experience
A Harvard Business Review survey sponsored by SAS, Intel, and Accenture identifies the ability to deliver a unique, real-time customer experience across all touch points as the best way for companies to distinguish themselves from the competition. The study describes three interrelated capabilities that allow businesses to apply analytics and insights to create effective customer experiences:
- Unified customer data platforms combine customer data from online and offline sources to extract insights that shape the customer experience.
- Artificial intelligence-based proactive analytics provide data collection and analysis functions that convert information about customers, marketing programs, and other business processes into actionable intelligence.
- Contextual interactions apply real-time insights to identify where customers are on their journey, such as browsing product reviews or visiting a brick-and-mortar store, and to coax them into taking the next steps toward the company’s desired outcome.
Customer Value Analytics
Customer value analytics is designed to help firms identify which customers are most profitable and which are least profitable.
These are among the metrics that customer value analytics applies to determine a customer’s value to the business:
- Historical value measures the value of a customer over time and compares it with other periods and other customers for the same periods.
- Current value analyzes a customer’s activity in a shorter time period to compare recent activity with past values to determine the impact of marketing campaigns, new offers, and changed prices.
- Lifetime value applies the analytics over a longer period by multiplying a customer’s average order by purchase frequency to show how the customer’s value has changed over time.
- Cost to serve compares the profit a customer generates to the cost of serving the customer’s support requirements to identify “service drain” customers: customers who buy few or low-margin products but require high sales administration and delivery expenses.
Omnichannel Marketing Strategies
A company’s marketing message must stay consistent as it spans platforms and devices. MarTech Advisor describes the four components of a successful omnichannel marketing effort:
- Identify the channels that customers are using most frequently, and increase their presence on those channels.
- Make sure that the marketing message is consistent across channels in terms of presence, communication, customer experience, and processes used.
- Customize the message at the most opportune moment. Personalization enhances engagement and brand loyalty.
- Measure the impact of marketing activities across channels, and continually optimize processes and messages to improve results.
Other Ways to Improve Customer Engagement
A challenge for marketing departments planning to implement a data-driven strategy is integrating the variety of data they receive from diverse internal and external sources. Much of the data must be cleaned and conditioned before it can be used, as MarTech Series explains.
The customer engagement tech formula allows marketers to assess available technologies in three categories:
- Decision engineering inverts the traditional marketing model by identifying decision opportunities first, and then running the data analysis. This allows marketers to focus on goals rather than on the analysis itself.
- Advanced analytics applies smart algorithms and other innovative analytics techniques to segment customers based on their specific lifestyles rather than on demographics. This improves the accuracy and effectiveness of customer profiles.
- Cutting-edge technology includes machine learning and other AI technologies, such as chatbots, that engage customers and manage their interaction with the brand. Chatbots have become successful because they’re convenient for marketers and widely accepted by customers.
5 Data-Driven Marketing Strategies
The more a company knows about its customers and potential customers for its products, the more successful its marketing efforts. The goal of data-driven marketing is to convert the business’s data assets into sales. Here are five ways marketers are taking advantage of the knowledge they extract from internal and external data sources about their target audiences.
Personalize the Customer Experience
The best way to get people’s attention is by tailoring content and online interactions based on their demographics, purchase history, online activities, and other information about them. For example, DirecTV created a personalized marketing campaign that targeted people who’ve recently moved, as marketing service Adverity explains.
DirecTV knew that people were much more likely to try new services when they moved to a new location. The company combined U.S. Postal Service records of change-of-address applications with a personalized version of its homepage that only those people would see. The result was a greater conversion rate for the personalized page than for the standard homepage, despite the latter offering a $300 gift card for new customers.
Coordinate Marketing Across Channels
A common technique for implementing an omnichannel approach to data-driven marketing is identity resolution. Web marketing firm Acxiom defines identity resolution as “the ability to recognize an entity, be it a person, place, or thing, along with associated relationships, consistently and accurately based on both physical and digital attributes.” The technique attempts to coordinate marketing across channels based on the individual characteristics, interests, and technology footprint of each customer.
Marketing Evolution describes a three-step approach to automating an omnichannel marketing campaign:
- Establish the data sources, which may include television, radio, mobile apps and alerts, social media, paid search, influencer campaigns, traditional press, videos, podcasts, and other media. Also consider how the target audience will use each source in relation to the campaign’s goals.
- Establish modeling and attribution, so marketers can confirm that data is properly categorized and displayed. Good decisions by marketers about campaigns and customer expectations rely on high-quality data that’s available when and where it’s needed.
- Continually improve the quality of the data by applying data checks and validation to confirm the accuracy and reliability of the information. Continual checks are required because data is constantly being updated and combined in innovative ways.
Use Predictive Analytics to Create an Ideal Customer Profile<
By integrating predictive analytics with account-based marketing, businesses are able to target accounts that match the company’s ideal customer profile (ICP). Digital marketing firm Leadspace explains that an ICP allows a business’s sales and marketing teams to seamlessly coordinate their efforts to “guide the best leads through the sales funnel.”
An AI-based solution for devising an ICP has three parts:
- Predictive analytics identifies behavior patterns in the data a company has collected about customers and potential customers. The patterns are transformed into intelligence that determines which customers are high quality (likely to be converted to sales) and low quality (unlikely to lead to sales).
- Quality data powers the analytics engine and models. The data must be timely, relevant, accurate, and available when and where it’s needed. Inaccurate and obsolete data will skew the model and reduce its usefulness.
- The expertise of team members must be converted into a form that can be imported into the machine-learning model. The interface that connects staff with the analytics engine must provide real-time access to the results of the analyses, so the model can be continually refined.
Apply Big Data to Track Marketing ROI
Converting big data into insight requires combining the science of analytics with the art of communicating the resulting insights into actionable intelligence. TechGenyz reports that for every dollar spent on analytics and business intelligence solutions, companies realize an average return of $13.01, which represents an ROI of 1,301%.
TechGenyz describes five ways data science is applied to increase the ROI of marketing campaigns:
- Break down departmental silos to promote the free flow of data throughout the organization. In addition, companies must ensure that the data is easy to integrate with other systems and share with internal and external sources, such as sharing social media demographics with affiliate marketers and internal search engine optimization (SEO) teams.
- Ensure that data streams are updated in real-time to promote fast action based on timely and accurate information. Include data “trails” in the stream to allow marketers to compare past performance of campaigns with current campaigns. Streaming analytics helps marketers identify new business models, product enhancements, and revenue sources.
- Apply visualization tools that simplify complex data and communicate the results of analytics in a way that’s easier for nonmathematicians to grasp. Visualization also helps data scientists and marketers discuss the results of the analyses and their implications for future campaigns.
- Conduct smart business experiments based on variations of marketing approaches to gain insight and discover alternatives. Even simple business experiments can provide keys to rapid revenue growth opportunities.
- Base marketing decisions on past customer data by using data-based tools to assign values to unknowns, forecast the potential for obstacles, and determine the best ways to avoid and mitigate risks.
Transfer Offline Data to Online Environments via Data Onboarding
Digital Doughnut defines data onboarding as the process of transferring offline data, such as postal addresses, telephone numbers, and in-store purchases, to online platforms for marketing purposes. Because personally identifiable information (PII) is used to connect data from the two realms, data onboarding entails anonymizing the data before it’s shared.
Leveraging offline data helps marketers better understand their businesses’ customers. It can help marketers target potential customers, and enables them to craft personalized messaging based on customer data.
What Do Marketers Use to Analyze Big Data?
The following are among the most popular tools and techniques used by marketers to collect data relevant to their marketing activities and convert it into actionable marketing intelligence.
Key benefits and features of Optimizely:
- It has a fast, scalable, and intuitive interface that facilitates data-driven decision-making.
- It smoothly integrates with analytics platforms, tag managers, content management systems, and third-party data.
- It performs multipage, A/B, and split URL testing of sites, mobile apps, and mobile sites.
- Its audience targeting options include data export, preview mode, campaign schedule, stats engine, and behavioral targeting.
Google Analytics, Google PageSpeed Insights, and Google Search Console
Google Analytics is a free tool that helps businesses understand how customers are interacting with the company’s websites. The service uses machine learning to extract insight from an organization’s data, including which customers are most likely to purchase a product and which have the highest revenue potential.
Google PageSpeed Insights generates a performance score that shows how quickly pages run on mobile and desktop devices. It offers suggestions for improving page performance, using lab data to help debug performance issues and field data to capture real-world user experiences.
Google Search Console reports on a site’s search traffic by showing which queries lead customers to the site. The service allows businesses to submit site maps and individual URLs to improve their placement in Google search results. Customers can also review the service’s index coverage to ensure that it’s up to date.
Adobe Creative Cloud
Adobe Creative Cloud combines several of Adobe’s cloud-based tools. It’s intended to streamline a company’s workflows and ensure that all of a marketing project’s team members and stakeholders are in sync. Among the over 20 apps in the Creative Cloud bundle are Photoshop, Illustrator, InDesign, Adobe XD for interface development, Premiere Pro for video editing, Dreamweaver for site development, and Animate for creating interactive vector animations. MakeUseOf offers five reasons why Creative Cloud is worth buying:
- It gives businesses a choice of four purchase plans.
- It’s an affordable option for photographers.
- New features are available immediately.
- Its cloud-based storage and collaboration tools boost productivity.
- It provides companies with “seamless consistency across disciplines.”
Stirista describes Crazy Egg as a “heat-mapping tool” that creates two-dimensional representations of data. Heat maps represent data values as different colors that illustrate the way customers interact with a company’s website. Crazy Egg calculates page views, the tabs and links visitors click, and other actions to give marketers insight into which elements attract users’ attention and which don’t.
Among the analytics tools Crazy Egg offers are an A/B tester for comparing different versions of pages; page editing tools for modifying elements on the fly; and a complete analysis of sites that indicates where site visitors come from, how they navigate the site, and where they’re running into roadblocks.
Marketers need to keep abreast of the hot topics in their product areas. BuzzSumo analyzes the most popular online content related to specific keywords, such as brands, technologies, and hot topics. The Next Scoop describes several ways marketers use BuzzSumo to promote campaigns:
- Monitor brand reputations by discovering all mentions on the internet and triggering alerts whenever a new mention appears.
- Create content that’s optimized for search engines and designed to attract an audience for your marketing for many months or years.
- Find the most frequently shared content related to whichever topic you search for. Results can be filtered by date, content type, language, word count, and country.
- Calculate content marketing ROI by measuring the level of engagement for every piece of published content, and average engagement by network, content type, or other category.
- View the most shared backlinks to a campaign’s content sorted by total engagements, domain links, or external links.
SEMrush is a SEO tool that helps marketers gather information about competitors by monitoring their sites for a range of site traffic sources, including direct, search, referral, paid, and social. Marketing service Location Rebel describes the service’s five toolkits:
- SEO Toolkit is used for keyword research, competitive analysis, page optimization, and link building.
- Advertising Toolkit helps identify optimal keywords for paid advertising that balance traffic building with affordability.
- Social Media Toolkit manages the company’s social media feeds by scheduling and tracking posts.
- Content Marketing Toolkit analyzes the posts in a marketing campaign and suggests ways to optimize the content to improve its search ranking.
- Competitive Research Toolkit lets marketers reverse engineer their competitors’ online operations to identify backlinks, traffic, and organic research.
Email marketing service MailChimp offers a marketing platform designed to help marketers learn more about their target audiences. Among its components are customer relationship management (CRM) software, an audience dashboard to help personalize marketing messages, and tags that can be applied manually or automatically to contacts.
Stirista describes MailChimp’s key features:
- E-commerce integration with a range of web services, including WordPress and Shopify
- Support for targeted audience campaigns, including automated follow-ups
- Personalized order notifications
- An extension for the Google Chrome browser
In addition, MailChimp’s predicted demographics feature forecasts the gender and age of contacts in the campaign’s audience and provides insight into their behavior, including their clicks, purchases, downloads, and other actions.
HubSpot combines management of content marketing, social media marketing, landing pages, SEO, and web analytics. In addition to a free CRM product, HubSpot offers paid services that include CMS Hub for managing content, Marketing Hub for boosting traffic and increasing conversion rates, and Sales Hub that automates many sales management functions.
PCMag.com lists the noteworthy features in Marketing Hub:
- Demographic information is pulled automatically from URLs and contact records as soon as they’re added.
- Users can send emails and make voice calls to contacts (in conjunction with Sales Hub), and log and save call information inside contact records.
- Users can merge multiple contact lists from other sources.
- Users can publish email blasts directly from Facebook, Instagram, and other social media.
- Users can test multiple versions of an email marketing campaign.
Data-Driven Marketing Benefits
Adopting data-driven marketing benefits companies by allowing them to craft more effective marketing campaigns, build brand recognition, and enhance customer loyalty. Rather than guessing what people want, marketers can tap information about consumers that they collect from diverse sources to base their marketing decisions on hard data.
Improve Media Buying, Customer Targeting
Data-driven marketing improves the effectiveness of a company’s media buying, targets the most receptive customers, and communicates relevant messages to customers. The combination of big data and AI-based analytics allows marketers to target customers with unprecedented accuracy.
Marketing service Criteo describes eight customer targeting strategies:
- Reach lapsed customers by offering them deals on the company’s top-selling products.
- Identify seasonal buyers, and predict when they’ll be most receptive to special offers on their favorite categories of products.
- Persuade offline customers to become online customers by offering personalized recommendations and online-only promotions.
- Enhance engagement with the company’s brands by promoting exclusive offers for loyal customers, providing high-value customers with incentives to join loyalty programs.
- Upsell on a previous purchase by offering discounts on matching accessories or other complementary products.
- Cross-sell based on the customer’s previous purchase via promotions on products in a similar category, such as tablets for laptop purchasers.
- Keep customers informed of new products, targeting frequent buyers of similar products.
- Promote upgrades to purchased products when updates become available.
Continually Update the Marketing Message
To keep their communications with customers fresh, marketers must continually update the marketing message by tweaking content to gain the attention of the people who are most likely to respond positively. Trew Marketing provides five tips for keeping a marketing message from growing stale:
- Make sure your site matches your message. Products and marketing strategies often change faster than elements on the company’s website. For example, the most important attributes of featured products should match the message in the company’s “what we do” description.
- Keep your message consistent. When the message is updated in one medium, the change must be represented in related information on all other platforms.
- Update auxiliary marketing material. For example, companies often have a standard pitch deck that introduces customers to the company and its products. Each time the market message is updated, rework the pitch deck and other marketing resources to match the change.
- Make sure staff members are informed of the new message. Prepare a presentation on the update that internal teams, new hires, and partners can view.
- Emphasize how the product meets customers’ needs today. Products evolve to meet the changing needs of customers. Data-driven marketing helps companies stay in tune with the problems their customers face so they can explain how the product solves their problems.
How to Use Data-Driven Insights
The business intelligence gleaned from data-driven marketing can be used to enhance the company’s brand, track competitors, and optimize pricing. Companies continue to discover how to use data-driven insights in new and rewarding ways.
Techniques for Enhancing Customer Retention
Data-driven marketing can improve customer retention efforts by making it easy to solicit and act on customer feedback. Marketing service Help Scout presents research-backed customer-retention strategies:
- Stand for something. Consumers establish long-term relationships with brands that share their values. Data-driven marketing helps companies communicate their values to customers.
- Share the company’s momentum. When a company develops a new product or enhances an existing one, it creates internal momentum that propels the business forward. Share the momentum with customers via the company’s marketing message.
- Educate customers about how to use the product. Make training part of the marketing effort by offering in-product onboarding, lifecycle emails, online training, and access to product experts.
- Reciprocate unexpectedly. Offering consistently good service is one of the greatest drivers of repurchases and recommendations. The marketing strategy should use data to be proactive in reaching out to customers to check in or simply to say thank you.
- Treat loyal customers like royalty. People appreciate a company’s efforts to make them feel special. Data-driven marketing extracts information about a company’s high-value customers that can be used to demonstrate how much the company appreciates their loyalty.
Using Machine Learning and AI to Automate Marketing Operations
Machine learning and other AI techniques are being used to automate some marketing operations, identify promising new market segments, and improve customer service in response to changing preferences and market conditions. Alan Sharpe describes three ways machine learning improves marketing automation:
- Employ dynamic pricing strategies allow businesses to offer flexible prices on products based on customer demand, market trends, and other conditions. Machine learning makes relevant, up-to-date data available to make dynamic pricing more effective.
- Use chatbots to offer 24/7 support that can be personalized based on what the system has learned from its internal and external customer data sources. Machine learning also helps personalize a customer’s shopping experience, such as the recommendations made by Amazon and Netflix.
- Gain lifetime customers by leveraging the insights into customer behavior and preferences that machine learning extracts from the company’s data assets. The more a company knows about its customers, the more accurately it can anticipate their future needs and behaviors.
Putting Data-Driven Marketing into Practice
In the coming years, all marketing will be data driven. Companies that are able to take advantage of the intelligence that can be gleaned from the information they collect about their customers will have an edge over the competition. More importantly, the brand loyalty and affinity that data-driven marketing encourages will pay dividends well into the future — at least until the next transformational technology arrives.
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