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Big data, artificial intelligence (AI), and machine learning technologies are rapidly changing global business practices. To remain competitive, brands need to embrace data-driven marketing. But integrating it into your business can be a challenge without company-wide buy-in. For data-driven marketing strategies to be as effective, companies need several components in place:
One of the biggest roadblocks to true integration of data-driven practices is the fear of failure and punishment. This method of marketing requires teams to develop creative, bold ideas, and be eager to learn from the experimentation process. Marketers can accomplish these goals if the leadership in the company places more value in optimization than in being safe. Here are some tips that can help you to integrate data-driven marketing into your company successfully.
In this article:
Adopt an Experimental Mindset
Implement Data Across All Relevant Departments
Track Progress with KPI Dashboards
Combine Data with Human Insights
Turn Data into Action
Examples of Actionable Insights
Embrace Marketing Automation Tools
Data for the Win
For data-driven methods to work, the company culture needs to support them. Values, incentive models, and practices must align and reflect the company’s prioritization of continual data-driven improvements. This process calls on all departments — not just marketing — to participate and understand the value of a data-driven approach. Remember, this is about rapid learning, which requires a fast-fail methodology and calls for regular, minor failures to achieve more significant wins.
Being data-driven is about starting with small experiments, figuring out what works, and scaling based on the results. Instead of choosing one creative to run, marketing teams can test multiples against each other. The process repeats for landing page headlines, email subject lines, and the like. With encouragement from company leadership, marketing teams can experience the freedom of experimentation. You will gain a sense of accomplishment from unlocking mysteries behind the data, and get excited about making rapid progress toward better results.
To maximize the benefits of a data-driven strategy, there has to be tools and processes to centralize the data. Companies also need to implement data in all aspects of the business. An optimization and testing process should cut across teams to include design, copywriting, content creation, social media messaging, site navigation, paid media, and so on. If your company stops short at tracking and analyzing only one of these components, it will miss the big picture and the real value.
Within many companies, departments and sub-departments segment into individual units that don’t communicate well with each other. This structure poses a problem for the data-driven model, which requires departments to collaborate, test, and improve together to achieve stronger results for the company as a whole. The process must involve all team members to make data-driven marketing work. It’s crucial to gain buy-in from all functional groups to realize the full benefit of optimization. Once this occurs, widespread data-driven culture can take hold.
A/B testing, segmentation and persona targeting, KPI dashboards, and opportunity forecasting are some of the key elements of the data-driven marketing strategy. By this time, most marketing teams are familiar and have experience with the benefits of A/B testing and segmentation. 90.7% of US Advertisers and marketers segment their data to better target and engage with their customers. However, spending the time and resources to gather data is just the first step toward making data-driven strategy work. For the information to be useful, marketing teams must be able to analyze the data successfully and gain insights that will help to direct future campaigns and marketing activities.
The KPI dashboard is one of the most important tools to give departments the ability to review marketing performance. Once the KPIs are defined, teams can gather data into a single dashboard for analysis. The challenge is that data lives in many places and is sometimes managed by different departments. According to a CMO Council report, 25% of marketers reported no connection between their data systems, and 15% reported a disconnect between new systems and their legacy data or infrastructure. Less than 5% of all marketers surveyed said that their systems were integrated and that data transfers easily between all points.
When data is siloed, it's challenging to gain helpful analytics and insights because it's impossible to see the big picture. Investing the time to build an effective KPI dashboard will help to tie these data sources together. Then, teams can gain complete visibility into marketing performance and progress toward company goals.
Sometimes, companies can take data analytics too far by letting the data make decisions. The insights that your company gains from ongoing experiments should inform your decision-making process, but not determine the conclusions for you. Businesses can't run based on analytics and algorithms alone. Human minds are required to process the nuances and dependencies that make up the larger picture. For example, just because the data points to option A, doesn’t mean that option B isn’t the better choice in the long-run, given upcoming changes in strategy or company values that the “machine” is unaware of.
Companies must transform the data they collect into actionable insights for the information to be useful to the company. Many marketing teams now using various tools to analyze and test practices, like Google Analytics and A/B testing for email subject lines. However, analyzing the results and applying those insights to future activities is the piece of the puzzle that seems to go missing. To complete the loop, marketers will need to coordinate between multiple departments and team members. If your company can master the data–insight–action process, there’s a good chance that you can surpass your competition.
Insights gathered from data collection can be applied to nearly any aspect of the business, from marketing to product development. Now that you have the power to harness big data, and gain actionable insights from it, get creative with how you can implement it. Here are some common uses for data-driven insights:
Create or improve buyer personas. Buyer personas are created based on real data from actual customers. As marketers continue to collect data over time, the findings could improve, add, or eliminate buyer personas.
Personalize campaigns. The more that you know about your audience, the easier it’ll be to personalize campaigns by focusing on benefits that matter to them and offers that will pique their interests. Data analytics can also suggest optimal dates and times for communicating with your audience and note their preferred methods for communication.
Use retargeting advertisements. Retargeting enables you to continue advertising to website visitors even after they leave your site or app. The best versions of these ads are highly targeted, reminding viewers about items left in their cart, or making recommendations based on other products that they liked. Four out of five consumers notice retargeting ads, and there’s a 900% increase in click-through-rates for retargeting ads versus regular ads. Retargeting is especially useful on social media since 30% of all time spent online is on these platforms.
With so much information to manage, it’s becoming necessary for companies to implement select marketing software programs to gain efficiencies. AI-driven ad platforms can help to optimize your retargeting campaigns with smart-bidding tools. The technology will automatically shift budgets to the highest performing campaigns to satisfy your marketing goals, increase ROI, and reduce costs.
Customize creative and improve content quality. Use information about your customers and site visitors to improve ad creative and content quality. Analytics technology enables marketers to test a large group of advertisements at a time, remove low-performing ads, and refocus on ads that earn the best results. When consumer needs change, those shifts will reflect in your data. As you track new and emerging trends in consumer behavior, your company can respond quickly with helpful content and improved service offerings.
Boost customer loyalty. Customer loyalty programs provide an excellent way for business owners to reward customers, earn loyalty, and efficiently track purchases. You can use this data to monitor which promotions and incentives customers respond to favorably, what their spending habits are, and how various segments or buyer personas behave differently.
A component of your marketing strategy should always be devoted to loyal customers. They set the benchmark for what a successful plan looks like, and they’re vital to providing social proof and referrals that potential consumers value. Take the time to thank your customers in the form of a friendly email or even a gift certificate or other token of appreciation.
Inform product development. Once you gather customer data, you’ll have a better idea of what your customers are looking for, aspects of your products appeal to them, and how some products or services may fall short of expectations. This data can be used to inform product development and either improve existing products or introduce new solutions.
The amount of data pouring in through various channels can become overwhelming to marketers, which is why the adoption of marketing automation tools is on the rise. One of the new measures of success will be how effectively marketing teams can leverage technology solutions.
Data-driven marketing is all about centering marketing efforts on consumer needs and continually improving performance. The better that companies can provide for their target audiences, the more effective their campaigns will be, and the faster their sales will grow. Insights from big data can inform every aspect of company development from new products and pricing, to ideal colors for a landing page. The power of data lies in its accuracy, relevance, and how well you implement data-based insights.
Originally published on December 18th, 2019, last updated on June 22nd, 2022.