Data-driven marketing is when marketing teams build their strategies based on the analysis of big data. This analysis will offer insights into customer preferences and broader trends that stand to impact the success of a marketing campaign.
While taking a data-driven approach to marketing was once rare, the multitude of niche media channels and evolving consumer expectations have made data analysis an essential process in modern marketing campaigns.
A data-driven media planning approach is now aided by the vast quantities of information that organizations have access to. 黑料社入口 teams collect data through the use of applications or various websites, and with good attribution modeling, can track each brand interaction along the customer journey. When all of this information is parsed and analyzed, marketing teams can see which creative assets drove more engagements, which channels offered the highest ROI, and more. Based on these findings, organizations can hone their campaigns to ensure the best customer experiences and the greatest return on marketing investment.
Modern consumers are inundated with brand marketing and messaging. As a result, they have become increasingly discerning of which messaging they will engage with. When using a data-driven strategy, marketing teams can drastically increase the chances that their target audience will click on their ad, join their webinar, read a blog post, or perform another action that drives a conversion goal.
Data-driven strategies improve customer experience and brand perception, as it gives organizations an understanding of consumer needs and interests. They also improve conversion rates, because the highly targeted messaging enabled by data-driven marketing is more likely to catch the attention of users. Some of the top benefits of data-driven marketing are:
Better Customer Experience
Data-driven marketing focuses on using in-depth consumer profiles to make the customer experience better. This is essential to success, as report leaving a website to purchase a product elsewhere due to a poor experience.A common challenge for marketers is determining where their advertising budget is being wasted. Data-driven marketing led with analytics tools allows marketing teams to discover which portion of the advertising budget is having the greatest impact on conversions or brand awareness. This is done by evaluating customer journeys using attribution models, such as unified marketing measurement (UMM). UMM looks at multi-touch attribution and media mix modeling to provide a comprehensive view of the path to purchase. Organizations can determine what moves prospects and customers down the funnel then allocate dollars accordingly.
Evaluating consumer data gives marketing teams insight into the types of creative, visuals, copy, and content that your target audience prefers to engage with. Delivering the right message 鈥� one that caters to personal interests and creates value - at the right time is essential to connecting with your consumers. Unfortunately, many marketers struggle to align their content with their audience, as evidenced by two key data points:
By diving into your analytics, you can find what messaging and which pieces of content are resonating with your audience. This can lead more effective product decisions and help you understand your clients.
Better Decisions
Overall, taking a data-driven approach to marketing allows teams to make more informed decisions, with agreeing that it is preferable to base decisions on data than gut instincts. Data analysis allows marketers to make choices based on real-world use cases instead of theories. However, data-driven marketing does not discount the emotional considerations that can go into a consumer purchasing decision. 黑料社入口 teams must evaluate data within a framework that considers rational and emotional decision making to ensure they are balanced properly in campaigns.
Data-driven marketing strategies stand to be positive for both marketers and consumers. However, there are a few challenges that can keep marketers from extracting the full benefits of their data or from reaching customers in an effective way.
Though consumers want personalized experiences, they don鈥檛 want organizations to know everything about them. Even more so, if they decide to give over personal information, they want to know how it will be used to their benefit. Consumers feel strongly about data transparency, with saying they will stop doing business with a company if they learn their personal data is used and collected without their knowledge.
When targeting customers with messaging or personalization tactics, companies should consider how they are providing value to a consumer. For example: making it easier for consumers to make purchases versus showing customers how much you know about them. Furthermore, marketing teams must be incredibly transparent with how data is collected and used 鈥� giving consumers the ability to opt out of data collection, especially in light of regulations such as and .
Poor Data Quality
In order to have a plan governed by data, you need to have the right data processes in place. This will ensure you are basing decisions and strategy on high-quality data that is representative of customer needs. If your data does not conform to data quality dimensions such as timeliness, accuracy, completeness, representativeness, etc., then you risk making decisions based on data that provides little visibility into your customers鈥� actual needs. In fact, almost half of new data records have at least one critical error in them and an HBR study could only loosely rate of data quality scores to be acceptable. With this in mind, before embarking on data-driven tactics, marketing teams must ensure they have data quality standards and policies in place.
Many companies are investing in big data (often spending millions), but have been unable to produce any tangible return on this investment. If you are collecting large quantities of data -- but not the right data 鈥� it will do little to inform your marketing strategy. 70 percent of marketing and sales executives have reported data-driven marketing as an important initiative, yet only about have seen a positive impact when investing in these solutions.
In order to get the most of their data, companies need to have the right personnel, policies, and infrastructure in place. This means data scientists that can derive insights from large datasets, processes to keep data clean, and the right software partners to sort, correlate, and process immense quantities of data. It鈥檚 a function of having employees with the right skills and software with the features to assist them in making the right decisions.
Implementing a data-driven marketing strategy takes time and resources as marketing teams must ensure the right policies and controls are in place. While marketing teams often find the process complicated, the returns are ultimately worth it.
Even if your company has the proper talent and technology in place, it can be difficult to understand where to start. 黑料社入口 teams need to be sure they have an in-depth plan in place before setting out, or should partner with a third-party team that can guide them on getting the most out of their data.
When developing a plan to implement data-driven marketing, especially at a time when global data collecting policies are becoming more stringent (especially in terms of GDPR and CCPA), consider the following:
Data-driven marketing is all about improving marketing success based on an enhanced customer experience, which is enabled by data insights. Customer experience is key to this equation. Each campaign designed with data should demonstrate a clear answer to the question 鈥渨hat is in it for the customer?鈥�
With this in mind, creating a sheet that promotes your products is not enough to encourage downloads. Consider the user and what they would find valuable. Based on the data you have, what is the problem your customer is trying to solve? Where in the buyer鈥檚 journey are they? From there, determine the most helpful piece of content or information you can provide.
Consumers are more likely to offer their personal information if they believe they will get better deals or more value out of their brand interactions. Make it clear to customers that they will receive something useful if they allow your organization to leverage their data and build user profiles. This may be personalized product recommendations or insider information in a newsletter format. Marketers must highlight what consumers will gain.
Be Transparent
Many consumers are concerned with how organizations utilize their data - ranging from invasive messaging to the risk of having their data stolen in a breach of your network. 黑料社入口 teams must be highly transparent with what information they are collecting, how they will use that information, and how it will be stored and secured. Additionally, give customers control to change their data or to delete their account. This is required by GDPR and CCPA. Maintaining a high-level of visibility into where data resides will allow marketing teams to track and modify data as requested by customers.
As stated, implementing a data-driven marketing plan can be complicated. This complexity is compounded when an important step is skipped 鈥� forcing teams to back track and lengthen their time to value for the program. Before beginning your marketing plan, review these key steps in the process and make sure you have a plan and the resources necessary to complete each one.
Determine Which Data You Need
This will depend on the goal of your program. If you are trying to build person-level user profiles, you will prioritize collecting consumer information. If you are attempting to track the path to purchase and customer journey, focus on attribution data. After you determine your goal, be sure you have established KPIs that will allow you to measure the success of your program.
It鈥檚 important to understand what you are trying to do with the data. Work together with your data science team to find any gaps that may exist in current dataset, and determine how to fill these gaps to measure KPIs and move forward.
Implement Data Quality Best Practices
Again, if your data-driven program is based on inaccurate or incomplete data you will not see positive results. In fact, you risk making changes to campaigns that move them away from the experience your consumers want. To minimize this risk, be sure to establish clear data quality policies. This will ensure you are making decisions based on the most current and representative data available. Have policies in place across departments to ensure each team is recording the same information in the same format. The top data quality dimensions to consider are:
Data-driven marketing can be used effectively to inform short-term performance campaigns or long-term brand building campaigns. Here are a few ways marketing teams can leverage data in their strategies:
Targeted Messaging
Attribution data can give your team insight into what types of messaging are the most effective at grabbing the attention of your target audience. This data informs user profiles with information like 鈥渞esponds to humor in ads鈥� etc. 黑料社入口 teams can generate this type of content and from there, AI-enabled platforms can serve this message to the right consumer at the right time 鈥� creating a personalized experience.
Better Branding
Data collection and analysis enables marketing teams to better track brand building efforts, which though important, are more difficult to quantify. Through branding initiatives, organizations get greater visibility into consumer values that they can speak to as a brand. Brand awareness and loyalty are essential to customer retention and long-term growth, but it can be hard to demonstrate this ROI to stakeholders 鈥� as they do not always correlate to a direct, measurable sale.
Using data such as leading indicators helps marketing teams understand what makes the brand resonate with consumers and find where they can take fast action to improve their brand health.
As a marketer, your job is to reach your target audience where they are. For millennials, this may be on television or on Snapchat. For older generations, this may be in magazines or newspapers. Understanding the nuances of which channels to invest in for certain audiences is essential to media spend optimization efforts. It ensures the most engagements for the least amount of money.
By leveraging attribution data, marketing teams can determine the highest-value channels by seeing how often an ad or asset was engaged with, and how big a role that engagement played in moving them down the sales funnel.
Finally, be sure to leverage data and analytics to determine the best times to run your campaigns. This is another crucial component to personalizing your messages to customers and optimizing spend. It answers the question: 鈥淲hat time of day or what time of the week are your customers most receptive to ads?鈥�
For a B2B company, this may be during working hours when consumers are actively looking for a business solution to address a current challenge they are facing. For retailers, this may be on the weekend before shoppers are planning to head to the mall for new seasonal styles, or for Black Friday shopping. By understanding when clients are more receptive to ads, you can better target potential buyers with information when they are ready to receive it.
Today鈥檚 consumer expectations demand data-driven marketing, especially when coupled with the ever-growing number of channels, applications, and devices to reach them on. Once implemented, these plans allow you to harness the power of your data by creating personalized customer experiences, optimizing spend, and driving higher ROI.