When selling products or services or becoming an affiliate marketer, the best way to approach all marketing activities is to be a data-driven marketer.
Data driven marketing ensures that you attract the right customers and get the right kind of feedback. However, using data for marketing purposes may seem daunting, especially for someone at the onset of a small business startup.
Keep in mind that behind all the intimidating terminology, a data driven marketing strategy is actually easy to comprehend.
Today, we’ll discuss the steps you need to take to convert your business into a data-driven company.
What Is Data Driven Marketing?
Before discussing the tabulation, let’s shed some light on the more general terms.
Data driven marketing refers to acquiring customer information and its use to improve products and services and drive sales.
Essentially, it is the approach that optimizes business communications based on data gathered from customers. This approach makes it much easier to create tailored marketing strategies for an increased or the maximum possible return on investment (ROI).
Benefits of Data Driven Marketing
If we look at traditional marketing, it has always engrossed in what people need and desire. Furthermore, it sticks to the practice of creating and delivering what people want to buy.
Traditional marketing only relies on existing market studies and assumptions about target customers.
On the other hand, data driven marketing focuses on acquiring useful information about audience targeting. This way, data-driven marketers can make fact-based assumptions to identify and anticipate what customers really want.
Compared to data driven marketing, many traditional marketing tactics usually involve trial and error practices. Businesses that still use traditional marketing often have to generate multiple strategies until they find the one that provides the best gains.
Furthermore, avoidance of important data can lead to lots of undiscovered opportunities.
Another advantage of data driven marketing is that practitioners access the information to engage with customers in a timely manner and with the right business pitch. Customer relations become better since the information helps marketers generate a more personalized customer experience.
With highly informative customer insights, data-driven marketers can focus on specific market niches and attract new customers. Also, since the data allows strategy formulation in real-time, marketers can innovate plans accordingly.
What Is Big Data?
Looking back to 2005, companies started noticing how large amounts of data have become freely accessible through social media platforms. Apps like Facebook, YouTube, Instagram, and Twitter have become incessant pools of useful public information.
During this time, open-source data centers have begun popping up across the globe. These open-source frameworks continuously store and analyze large volumes of new information, which we now call big data.
Oracle defines big data as having five V’s: volume, variety, velocity, value, and veracity.
Volume
Volume refers to the amount of information stored and analyzed in bulk. Big data comes in high volumes of unstructured, unsorted information.
Variety
There is so much variety in data for every set of data in large volumes. Data types include audio, video, and text, each requiring unique processing to export valuable metadata.
Velocity
Big data comes fast. Hence, businesses and companies have to act on it quickly.
While some products and services stay in the market for much longer, some internet-based products require real-time assessment and action.
Value
Today, any form of information is worth something. However, any information can remain useless unless we discover its value.
Veracity
In addition, we can only put value to information if we can verify it. Much of the data streaming from metadata after metadata will only remain useful if it remains high quality. Essentially, extractions and interpretations from big data should remain factual.
The Importance of Big Data
With big data, getting complete and factual interpretations from more information becomes possible. Since interpretations come from more sources, there is increased confidence in the data.
If you look at multinational corporations, they typically have a whole department that focuses on analyzing and interpreting big data.
All analyses and interpretations lead to a deeper understanding of a target audience. They also enable companies to build a solid and more personalized connection with potential customers.
In addition, big data help businesses uncover more, if not the best, marketing channels for engaging with customers.
How Does Data Driven Marketing Work?
Now that you understand the importance of data-driven tools and big data, you can push your business to become data-driven. The point is to make the data accessible to everyone in your organization, especially those who can put the data to good use.
There are always data, marketing analytics, marketing insights platforms, and marketing automation tools for supporting all types of marketing efforts and decisions.
However, that data needs collection and sortation before analysis. Moreover, it has to go to the right people in your organization so that they can use it to build your marketing strategies.
To convert your business into a data-driven company, consider the following steps:
Step 1: Team Assembly
The first step toward owning a data-driven business involves handling chunks of information. Your marketing campaign will only be as effective as your marketing team.
If you have departments within your organization that does not normally contribute to marketing performance, much of the useful data usually remains shelved. These departments are weak points in your organization, and moving from traditional marketing to data-driven tools will make it more apparent.
Your marketing team should allow interdepartmental and interdisciplinary collaboration if you want to put all important data to good use. In other words, you need to assemble creative teams into one assembly.
Instead of picking a manager from each department, choose someone willing to learn the ropes of data acquisition and interpretation.
Step 2: Data Gathering and Consolidation
Once you have assembled a team of interdisciplinary data interpreters and marketers, all data must stay in a single repository. This repository will stand as your marketing hub’s resource center, and it shall store customer buying patterns and e-commerce metrics.
Additionally, it will hold all customer communications, user search trends, and other customer behavior.
All the data need to be in one place so that every team member who can use the data can easily access them. However, the data must also be organized and held in sub-repositories created under the department that initially acquired it.
Also, apart from assigning sub-repositories for interdepartmental sharing, it is important to consider the following factors:
- Online and Offline Access
Successful companies keep iterations of all acquired data both online and offline. While online access enables smooth information interchange, offline data storage ensures a stable repository unaffected by online disturbances.
- Customer Relationship Management (CRM) Platforms
Integrating a CRM platform into your business model guarantees that you have a steady stream of data from your customers.
CRM platforms will help your marketing team analyze customer reviews and increase your website’s customer engagement rate. Furthermore, interpreting customer support conversations will allow you to extend the customer life cycle so that they keep coming back for your products or services.
- Mailing List
A ton of significant data also resides in your company mailing list. Like CRM data, your digital marketing team must centralize email lists for marketing messages to produce more personalized experiences for customers.
With personalized messaging, you can notice a gradual decrease in your mailing list’s unsubscribe rate.
- Online Product Catalog
Another thing to consolidate is your company’s creative assets. Copies of your precision marketing and advertising tools can also stay in your data bank.
This practice ensures that each team member will have an accessible reference when queries arise during audience engagement.
Your website can acquire a click-through rate to push more marketing ROI with audience engagement at optimum performance.
- Insights – Marketers
Your company should also consider cataloging the effort of marketers in your organization’s databank. Every time a team member comes up with complex models or any innovative marketing approach, it would be helpful for the entire team to access the same information.
Customer insights consolidated with marketer insights can make advanced marketing analytics a gold mine of e-commerce metrics that can improve your sales.
Step 3: Success Metrics Establishment
After assembling your team and setting up your centralized data repository, you can begin to formulate and evaluate your criteria for ROI. Having criteria ensures that you keep your goals intact throughout the entire marketing process.
Here is the procedure for computing the value of Marketing ROI:
- Subtract total marketing cost and organic sales growth from total sales growth.
- Divide the difference by the total marketing cost.
Ambitious marketers will target a marketing ROI ratio of 10:1, but, as a general rule, it should typically reside around the 5:1 range. Bear in mind that anything below a 2:1 ratio is not a profitable marketing ROI.
Nevertheless, consider that there are also challenges for measuring ROI.
- Simplistic Metrics
First and foremost, marketing metrics are too simplistic; some external factors tend to be unaccounted for, such as historic weather reports and weather forecasts. Other considerations include seasonal trends, recurring events, and holidays.
- Focus On Short-Term Results
Secondly, marketers tend to focus on short-term results. More often than not, we only look at click-through rates, likes, and shares.
We should be wary that some marketing goals take months, if not years before we can notice an effect. Success metrics should align with the overall duration of a marketing campaign.
- Multiple Marketing Channels
The internet has opened up a wide array of channels for businesses to do their marketing, which is on top of all the available offline channels. If we focus ROI measurements on specific channels, our success metrics will not be as accurate.
The good thing is that many platforms provide unified marketing measurements so that disparate measurements become cohesive.
- Multiple Touchpoints Fronting a Buying Decision
Before customers proceed to a final buying decision, they go through several offline and online touching points. The only way to fully quantify marketing ROI is to account for the effects of all touchpoints across the marketing sequence.
- Outdated Attribution
Last but not least, misattribution can alter the accuracy of ROI success metrics. Misattribution results from outdated attribution models that neither provide granular insights nor indicate the impact of external factors and offline channels.
Step 4: Audience Identification and Analysis
After ROI criteria evaluation, the fourth step would be audience targeting. Identify and analyze your customers by ensuring that data revolves around them.
Otherwise, the data you collected would become useless, and it can even prevent returns instead of promoting them. Take advantage of all available information to understand your customers and the value they bring.
- Define Audience Segments
Using CRM information and demographic data, categorize your audience members. Integrate market shares, purchase history, and consumption of website content marketing.
If you can divide your audience into more specific segments, it becomes much easier to personalize all marketing strategies.
- Identify the High Rollers
Your highest paying customers are responsible for a large portion of your company’s profits. If you can learn to identify the most common customer behavior among them, you will be able to attract the same type of audience for future sales.
- Look for Similarities With High Rollers
As you look for your highest-paying consumers, consider engaging with customers who have similar behavior patterns. If you can continue communicating with them, there is a fat chance that they’ll convert to the same high-paying customers.
- Create Customer Personas
In traditional marketing, you are already subconsciously creating consumer personas based on your assumptions and initial observations.
With data driven marketing, you can create more accurate customer personas that give you real insights into the behavior patterns of your target audience.
Step 5: Prediction and Planning
The best way to predict and plan your marketing strategy is to create a touchpoint map for your personas to move through. Customers having those personas should be able to breeze through each step of the customer journey.
Step 6: Trial and Evaluation
Once the customer journey map is complete, give it a trial run, but do not launch the entire campaign.
Observe how customers react to it, and look for points that need improvement.
Remember not to fish for leads. Instead, analyze available data to diversify your understanding of customer behavior patterns.
Step 7: Deployment
As soon as you have had successful evaluations of your trial run, you can proceed with launching the entire project. Begin with your advertising campaign, and always keep your marketing analytics and campaign monitor in check.
Making Wise and Calculated Marketing Decisions Is Possible
Marketing decisions become critical when company finances are at stake. While you may have funds for multiple marketing strategies, it may take longer for your business to turn a profit if you do not start using big data now.
With data driven marketing, any marketing campaign becomes more streamlined, if not flawless.
Marketing leaders will always stand for the benefits of digital marketing analytics, social media metrics, and performance metrics in data-driven advertising. These strategies are approaches to marketing used by the biggest companies to span the globe.
Data Driven Marketing FAQs
1. What is a data-driven approach?
Data-driven approach refers to strategic decisions based on interpretations of analyzed data. It enables businesses and companies to examine and organize information to serve consumers and customers better.
A data-driven approach is a prelude to a customer-centric approach. With all the organized information, businesses can contextualize or personalize their messages to prospective customers.
2. What data is needed for marketing?
There are at least three types of data marketers can use for marketing: customer data, financial data, and operational data.
Customer data refers to a particular target audience’s names, email addresses, purchase histories, and internet searches. Financial data will come from a company’s costs, margins, sales, and marketing statistics, but it can also come from a competitor’s financial data.
Operational data refers to logistics and customer relationship management information that can help reduce costs and improve performance.
3. What useful data can marketers gather from competitors?
Marketers can study the current promotional campaigns and product offerings of competitors.
Gathering such easily observable information from competitors can bring a company to a higher level of customer engagement. Innovating on data from competitors ensures that a company stays at the forefront of customer relations and profit generation.
4. How do marketers use data to identify problems and issues?
Big data is essential for determining whether a product or service is successful or not. It gives marketers a vast resource of points for improvement and reasons for product or sales failures. As long as a marketer keeps an eye on products, customer service quality, and supplier capabilities, it would be easy to find where to make adjustments or changes.
5. What are common barriers to becoming a data-driven company?
We can name five barriers that can prevent a company from becoming data-driven. They include inactive data innovation, lack of operationalization, poor integration, poor interdisciplinary collaboration, and ineffective data governance.
Data gathering implementation should remain active at all times, and innovation in the use of data should be constant or improved. If you let technology control data gathering without supplementing it with analysis and interpretation, you will be wasting a ton of big data.
Operationalization only takes place when concepts turn into actual action steps. Without successful implementation, essential data losses its value and becomes obsolete.
Once concepts come into play, they should integrate seamlessly into existing tools. Even if solutions get implemented perfectly, they go to waste if AI systems and implementors don’t use them as planned.
Interdisciplinary collaboration is the biggest challenge companies face during data projects. If various departments within a company cannot work together, big data pools can go down the drain. Of course, team management tools play an important role in a successful organization.
Finally, data governance could be a problem even with big data freely available. Companies must learn not only to access data but also to sort valuable data from the trash.
