B2B Enterprise: B2B Analytics and its Scope in Industry (Series Part – 18)

B2B Enterprise

Here we are representing the eighteenth part of B2B Analytics and Its Scope in Industry. This time we are going to discuss B2B Enterprise. Let’s go through the b2b marketing report and know how B2B commerce platform is improving and stepping ahead to make it better. B2B marketing analytics can play a vital role in the e commerce business to business; b2b platform. For our previous report on B2B Analytics click here.

Let’s take a short glimpse of Enterprise B2B. Business-to-business “B2B” is a type of transaction indulge in between the manufacturer and wholesaler, or between a wholesaler and a retailer. B2B is associated with the business that is in between the companies, not in lieu of between the companies and individual consumers. B2B denotes the transaction of a business with another. Transactions at the wholesale level are usually business-to-business while those at the retail level are most often business-to-consumer (B2C). B2B is an Internet Business Model that includes providing products by giving a solution to online business services. This data can be effectively used in the b2b marketplace, providing the services to the right consumers and increase the business to business sales.

Enterprise B2B Overview:

  • Enterprise B2B: 780 billion USD
  • Annual Growth: 7.7%
  • Companies: ORB Intelligence, Leadspace, Infovision, Businessol

B2B Business Ideas:

Predictive analytics firms largely position themselves as platform and infrastructure rather than standalone applications. Predictive analytics firms largely position themselves as platform and infrastructure rather than standalone applications. Not to say that CRM, marketing automation or other app makers will buy or build predictive applications. However, predictive technologies and the data science within them are probably more complex than the functions within which they would embed. While every B2B companies may have to think about integrated predictive analytics, not everybody will make the same decision in the end. Right now, many companies build standalone platforms but use cases are always tightly coupled with other products. Predictive solutions will eventually be less of a standalone b2b portal platform and more of an integration with products from within.

This will be accelerated by companies like Amazon AWS and Google Services building (machine learning predictive) components into their developer platforms. In addition, these companies have an advantage because it’s more likely that machine learning predictive applications will be developed and tuned for specific utilization with open source libraries and techniques, commoditizing the whole space, according to Maynard. Other platforms pursuing these b2b marketing strategies including Microsoft Azure. However, not everyone believes that predictive analytics will become a commodity. Predictive analytics are specific to particular b2b domains, need strong data beyond email addresses; b2b websites in reference to customer companies, that will provide more value to business leaders— b2b sales, b2b marketing, b2b operations—than data scientists and must have wide availability.

An emerging category where predictive analytics finds a foothold comes by way of b2b marketing software like customer experience (CX) software. With the availability of customer big data, predictive analytics has become an important component of many cloud applications, b2b marketing technology and b2b software companies including the software CX management. For CX data analysts, that means not manually linking data and exporting it to an external tool for statistical processing to create visualizations for decision makers.

Business to Business Marketing Analytics:

Predictive analytics for B2B sales and marketing has certainly “crossed the chasm”, but it’s still in the early adopters’ phase of the product development lifecycle, and will continue to mature. We are already seeing predictive analytics adopted by more b2b enterprise, as shown by the recent Salesforce’s recent State of Marketing report where they found that 79% of high-performing teams currently use predictive intelligence. This is why larger vendors, like HubSpot, are entering the market with watered-down versions of predictive scoring, which is helping to broaden awareness for the opportunity.

However, the lowest common denominator approach also adds more noise, confusion and risk, which makes some B2B organizations sceptical about predictive as they try to figure out how it can improve their sales and marketing efforts. What they really need is actionable intelligence from the data vs. vast quantities of data that they don’t know how to operationalize.

Predictive scores are just one criterion to bring into sales intelligence and rich, descriptive profiles of ideal customers. Profile management, on the other hand, is a foundational technology that helps businesses overcome the difficult challenge of effectively managing segmentation and targeting in a data-immersed world. Sophisticated profiling can help enterprises orchestrate their data by telling marketers who are the most ideal target profile is for a particular campaign, and then sending the relevant list to the recommended engagement platform for action.

As the market evolves, it’s become clear that predictive scoring tools are not enough, and people want more transparency and control (for good reason). Across industries, machine learning outputs in general just aren’t that actionable on their own, and often end up as an incremental optimization vs. end state products. What our industry is missing is a guide that tells marketers and sales what to do next, vs. just giving them a score or a data entry system like Salesforce or Marketo—this is the gap.

Identifying the Achilles heel of successful B2B enterprises read more here.

B2B Enterprise Customer Analytics:

Many b2b enterprises are taking advantage of customer analytics software to create a powerful, personalized customer experience and improve their bottom line. Analyzing the vast amounts of data created by your customers is a process with tremendous promise for enterprises, offering the ability to personalize communications, predict customer churn (and take rapid action to prevent it), segment customers by demographic data, drill-down to specific channels or segments for custom analysis, and even make predictions about the impact of specific actions through the use of modeling.

With so many possible uses for b2b customer analytics, it’s not uncommon for b2b enterprise marketers to feel a bit lost. Where do you begin? What’s the best way to put customer behaviour analytics to work for your company that will produce the greatest ROI? Understanding how b2b enterprise customer analytics works and how it can be used to boost the bottom line is critical, as marketers are often faced with proving to C-suite executives that their investments in marketing technology are profitable.

B2B Enterprise Marketing Strategies:

What’s preventing b2b enterprise marketers from operating at customer speed? The prevailing Industrial Age mentality certainly isn’t helping. We’ve been conditioned to believe efficient procedures are the key to responding faster (and better). However, these are the very elements that may be slowing us down. Sometimes we miss opportunities to engage with buyers because we’re fixated on executing rigid processes rather than delivering fluid responses. This doesn’t cut it in a digital world, where customers are in control and B2B marketers are obliged to “pull” them in.

B2B Analytics

Without question, becoming agiler would be a plus for b2b marketing. But even better would be what Nexxworks CEO, Rik Vera, suggests: Focus primarily on the customer experience, rather than internal (industrial) processes. Marketers need to “liquefy” customer responses, says Vera on his company’s blog. The key to doing that, he claims, is to be flexible and informed in the moment, using interconnected data and customer analytics to shape your answers and guide your organization’s acceleration. Sticky products are just as important when you’re working with the b2b enterprise. Organizational buyers have tons of options, which means that a tool that gets the job done and delights its users will have a huge advantage.

B2C apps have been using analytics to build better user experiences for years, but B2B analytics is a bit trickier. Good B2B analytics involves both segmenting your users based on the actions they take—behavioral cohorting—and segmenting them based on their unique context for using your app—where they work and what they do.

Increasing Growth in B2B Enterprise Segmentation:

The problem with B2B analytics, historically, has been that the data you need to run good analytics is hard to get. To understand why users drop out of onboarding and stop coming back to your product, you need to segment your users by the different variables that affect how they start using it in the first place. A FinTech growth marketer isn’t looking for the same kind of results as a sales lead at a major Fortune 500 company, and you shouldn’t treat them the same. Using that customer data in your analytics will help you understand who is getting value out of your product, who needs help getting to that “Aha!” moment, and who your product should really be for.

B2B Enterprise Sales Analytics:

According to Forrester research, as much as 90% of the buyers’ journey may be complete before they contact a sales person. During that time, marketing is the key function to identify and mature the prospects for the purchase cycle.

Now, this is an interesting piece of information which can completely change the traditional methods of selling. The traditional methods of selling starting from lead generation from multiple sales channels to different stages of the purchase cycle. However now this needs to be evolved to a new format where the lead generation starts much before.

In this context, the marketing matrices and b2b commerce analytics become very critical. when I say analytics its not only data from your existing customer base from traditional methods but also information about buyers of all sorts. It could be their interests, things that they like on Facebook, what they follow on social media or type of b2b content marketing they follow. This whole can be clubbed to generate an evolved form of analytics which can be very useful for lead generation. For this the marketing analytics platform should necessarily have the following:

  • Analytics platform ( data from existing customer base from multiple sources)
  • Automation platform
  • Sociograph platform ( data from social media)

B2B and B2C Marketing:

There are cases, where b2b and b2c companies have developed/partnered to create such platforms and got benefitted significantly. Using these platforms also gives you a much better visibility on your sales funnel/pipeline.

 

In an age of social media, analytics, b2b marketing agency and Big Data study revealed that while most companies have invested in salesforce automation tools, training, and analytics, they still have significant “room for improvement” in several key areas including the following:

  • lead generation strategy and tactics
  • online conversion architecture
  • salesforce consulting skills and competencies
  • access to the right level within the customer organization
  • forecasting accuracy
  • responsiveness to custom RFPs
  • performance versus process metrics
  • creativity in sales and solution creation

 

Finally, as b2b enterprise companies looking to optimize their selling capabilities, the b2b advertising strategies must keep in mind three key trends that will dictate how well their optimization efforts will pay off in b2b and b2c marketing:

  • Informed buyers: Today’s buyers are more informed than ever. This means sales representatives must clearly understand the needs of the buyers (jobs-to-be-done), present a solution that clearly articulates the value and meets those needs. They must be creative and innovative solution builders. Thought-leadership and technical leadership are both required.
  • Availability of Data: The volume of business data continues to mount exponentially. Leveraging the right set of customer interaction data for pipeline generation and opportunity management provides leading b2b marketing firms a clear competitive advantage.
  • Technology: The role of technology in selling continues to grow. Companies must integrate technology and drive its adoption to maximize impact. But don’t make salespeople spend too much time on form-filling activities. Techb2b must maximize time spent engaging the customer.

 

Previously we discussed “Beauty and Wellness Industry“, the seventeenth part of the series “B2B Analytics & its Scope in Industry’s”. Earlier we discussed seventeen industry in B2B Analytics were  “Food-Chain Industry”; which was the first part, “Banking Industry”; the second part, “Healthcare Industry”; the third part, the fourth part was Manufacturing Industry’s a part- “Food Manufacturing Industry” and “Discrete Manufacturing Industry”; the fifth part. The sixth part we discussed the “Education Industry”; the seventh part was “Insurance Industry” and eighth part was “Government Data Analytics”; ninth part was “Retail Industry”; tenth part was “Transport and Logistics Industry”; and the eleventh part was the “Telecommunication Industry”. Twelfth Part we discussed was “Media and Entertainment Industry”;  the thirteenth part was “Consumer Packaged Goods Industry”; the fourteenth part was “FMCG Industry”; the fifteenth was “Pharmaceutical Industry” and the sixteenth part was “E Commerce Industry”.

In our next edition, some more interesting industries to be left for the discussion be ready to take a deep insight and we will discuss a lot about them. Stay tuned to us for further more deep insights in the industry. The next part will be out soon.

 

For customized market research reports for any kind of startups and business in any industry, you can contact Craft Driven Market Research team here directly.

 

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