How Big Data Makes a Difference in B2B Marketing
How “Big Data” Influences B2B Marketing
Information acted upon is power.
Data is a crucial part of any business. Without it, you would be relying on guesswork and trial and error tactics. Everything revolves around data. Even our five senses are data-gathering machines that help us navigate through life.
Big data has become a buzzword these days. Many B2C and B2B businesses are heavily capitalizing on it. There are different ways in which you can use it to gain a competitive advantage. One big area is sales and marketing.
In this article, we will take a closer look at how big data is reshaping the marketing efforts of B2B companies. There are opportunities but also challenges in this new environment. Next, we will examine the connection between big data and the ABM approach which is the most popular B2B tactics these days.
How can “Big Data” help exactly?
If you had to summarize “big data” in one sentence you could say that big data offers a possibility to create both high impact and highly personalized marketing campaigns by offering deep insights into both the market and the consumer.
By having this deep insight, you will be able to create highly accurate and responsive campaigns. Knowing who to target, when, and with what message and price are the key benefits “big data” provides.
How does this function?
Big data is essentially any collection of data that can’t be effectively analyzed by existing traditional models of data. Since data can be in both structured or unstructured formats, and from numerous channels, you first need to make all the data accessible and usable. After, data analytic techniques can be utilized to effectively process and analyze all of the data. These actions are usually achieved by using a big data platform.
Examples of data sources can be competitive analysis and market information, marketing research, product knowledge data, website traffic data, social media posts, press releases, call center logs, customer feedback, device data, 3rd party data, consumer sentiments, and transaction logs.
How can big data affect marketing?
Marketing and sales departments were historically divided, and they still are. They have different targets and often come to a disagreement regarding several topics. For example, is the lead qualified enough and who’s at fault because they didn’t convert to a customer?
This tendency can be seen across the entire ecosystem of the company. Indeed, a company is just a collection of different systems, much like a large Fortune 500 company operates. In the case of an enterprise, these systems can be the ERP, CRM, Product Information Management System, Order Management System, and Marketing Automation System.
This is the old, so-called, silo approach to departments. Big data analytics helps us to turn all these different systems into a unified framework with a precise collective set of goals. All these systems are sources of data that can be used to create a perfect go-to-market strategy, set up different marketing segments and audiences, or to guide targeted advertising strategies and campaigns.
Big data is reshaping the anatomy of a company. In order to leverage the knowledge and data, all departments of the company should be integrated and interconnected.
Knowing where to focus marketing efforts is important. With big data, you can achieve more with precise targeting, lead generation, and increasing sales. Combined with predictive analytics and AI methods, big data can help us determine where the customers will be, how they may act, and what they may “do”. This responsiveness translates to better demand generation, the ability to create new markets and segments, while enhancing and optimizing both multi-channel and omni-channel marketing experiences.
Customer analytics, search engine optimization (SEO), search engine marketing (SEM), email marketing, Push/SMS/mobile app marketing, and digital ad platforms are just some of the areas where big data brings a competitive advantage.
Opportunities for B2B companies
Providing a unified and exceptional customer experience across all channels is the end goal that will increase your b2b sales. Great “above and beyond” customer experience can be achieved through personalization and customization.
Through personalization, we come to relevance and precise targeting. A simple example is a webpage, offer, coupon, or ad that displays a different message for an existing visitor compared to a new website visitor. If you are running an ecommerce business, you can deliver a perfectly timed discount, cross-sell, or up-sell campaign. This strategy or variation thereof, can apply to many types of businesses, webshops and digital storefronts and marketplaces are typically the platforms that get the most out of these personalization marketing strategies.
Another example is the ability to customize the price on a customer-product level. Again, highly valuable for any B2B eCommerce business since each customer is different. This wouldn’t be possible without the automated stream of data. You can also optimize the order and reorder processes by using the information about the previous purchase.
Some companies structure their entire business around data to produce something called the ultimate product. The ultimate product or service is that which came as a direct response to customer insight. By analyzing every piece of information to determine the most beneficial set of features, the product is guaranteed to be a success (if everything else goes well).
Challenges for B2B companies
If you compare a B2B and B2C market you will spot a tangible difference. Whereas you can have millions of users in a B2C market you have a lot less in a typical B2B setting. This in effect is the main challenge for B2B companies.
The number of customers influences the quantity of data, and the quantity of data influences the ability to draw conclusions. However, eCommerce businesses are an exception here. Although still having far fewer customers than a B2C store they can produce a large enough pool of actionable data. Again, this is not a rule.
Big Data and ABM strategy
There is an interesting aspect of a B2B market...
The number of customers is not large, they usually require highly customizable services, and prices will differ from one account to another. The usual approach of segmenting different customers into customer groups starts to lose its purpose since every customer is an island for itself. This line of thought has led to the creation of the ABM approach.
The ABM approach is the most popular B2B tactic. It stands for Account-Based Marketing. If you are not familiar with it, ABM is a business marketing concept that treats every account as a market. This approach represents a shift from the usual MQL (marketing qualified leads) strategy.
The major prerequisite for ABM is to have good, quality data. To identify key accounts and targets companies, while gathering strong firmographic and demographic data. After they have identified their markets they should reach them across a variety of channels and deliver relevant content.
Can you see the pattern? ABM is the most successful approach for small SMEs and other B2B players and it will almost always translate to an increase in B2B sales. However, that’s not possible if you can’t obtain quality data. The kind that big data platforms can generate.
Conclusion
Every company in every age has its own unique challenges and opportunities. We all know what happened to those who acted upon them. Luckily with the aid of the right technology, even the relatively small B2B companies have a chance to compete against bigger players.
The key is to have good quality data.
The last few years have seen a growing trend among marketers. There is a rising demand for data quality. Each year more marketers list the quality of their data as a determining factor of their go-to-market strategy.
If you don’t know where to start from, we as a digital marketing agency can help you.
We are experts for B2B digital marketing and our main goal is to help you set up a successful ABM strategy, which you can’t do without quality data.
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