B2B companies are now implementing digital selling to the forefront of their business strategies, a strategy which B2C companies have been doing for some time.
The world of commerce is now taking place on the internet. Successful B2B businesses are embracing the digital era and implementing the strategies that buyers expect. While it is certainly the digital era, it is also the era of the buyer. The internet and the access that it provides to an abundance of information has afforded the modern buyer with greater autonomy. Unless you provide what the buyer expects, and at their pace, they move on. So, how do B2B businesses successfully predict the buyer’s needs?
Data analytics and business intelligence (BI) are core components of the successful modern B2B business. Everything that you do online now leaves a digital footprint. Big data analysis can optimize a business’ strategy from the inside out. Staff performance, customer journeys, and marketing strategies can all be approached in a smarter way using the trends and patterns uncovered by big data analysis.
Big data analysis is a complex procedure that requires a consolidated database and document management along with analytical tools such as key performance indicators (KPI). The business applications of today’s B2B market are scraping large amounts of data from multiple sources such as customer infographics, marketing trends, and performance data from the company’s website and applications to the performance of the product itself.
Business-to-business (B2B) companies are embracing predictive analytics in their marketing strategies. Unfortunately, this does not include a sage sat behind a crystal ball, and it is not as dependable as a mad scientist and his time machine thrust into the future.
SAS has defined predictive analytics as “the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data.” It is all about identifying certain patterns in their data and using it to gauge probability.
It is becoming increasingly important to B2B companies to gain insightful information into their strategies in order to stay ahead of the competition. This is good news for the services that provide B2B companies with the tools to make this process easier. Gartner has predicted that they will experience double-digit growth over the coming years.
The most common techniques of predictive analysis are:
- Regression analysis
Regression analysis investigates the relationship between a dependent and independent variable or variables. It is a predictive modeling process used to identify causal relationships between variables in order to prove or disprove a hypothesis.
- Data mining
Data mining is an essential stage in predictive analytics. It consists of examining large amounts of data sets to discover patterns and uncover new information. It utilizes a combination of techniques including machine learning, statistics, artificial intelligence (AI), and database systems.
- Machine Learning (ML)
ML uses deep learning algorithms to uncover hidden insights in large data sets without being programmed where to specifically look. ML techniques are becoming increasingly popular in mainstream media. Applications such as Netflix and Spotify recommend music and television or movie content based on what you have previously watched or listened to.
Big data analysis tools are becoming more and more accessible. This software is no longer just being used by data scientists. Data analysis tools and ML are increasingly accessible to everyday businesses and their staff.
In B2B companies, salespeople are now tasked with analyzing lead data while marketing teams are using customer demographics and social trends to better understand and target their key audience. With the predictive analytical producing insights, it takes expertise and trial and error to understand how to apply them to achieve the goals of your business.
Predictive analytics tools are evolving alongside BI. Companies are developing tools with increased levels of usability and user-friendly interfaces that ease the process of making sense of large volumes of data. They make the large data sets smaller and easier to analyze.
Using data connectors, these tools provide the capability of incorporating data from particular data sets into your business queries. Power BI is Microsoft’s contribution to the growing BI analytical market.
Forrester’s Senior Analyst of B2B Marketing, Allison Snow, has identified three B2B marketing uses of predictive analytics:
1. Predictive scoring
This aids B2B sales and marketing teams to identify the potential customers that would be more likely to become a lead. This helps teams to prioritize their time and effort into generating that lead into a customer.
2. Identification models
These models use existing customers as a basis for building a profile of future prospects. It works on the presumption that if existing customers have purchased the product or use your service and are satisfied, that individuals with similar demographics could be identified as potential customers.
3. Automated segmentation
Personalization is becoming an important component of digital selling. Segmenting consumers by needs, business size, and your business goals can shape your marketing strategy, and it is made a lot easier using automated segmentation. Sales and marketers can now develop personalized messages to target potential consumers’ interests and company goals.
Direct marketing is the most common, and perhaps one of the easiest, applications of predictive analytics. Direct marketing is, essentially, one-on-one marketing. This consists of contacting customers through emails, text messages, direct messaging, and paper mail.
The most popular form of direct marketing is email and it is remaining a valuable communication tool between B2B business and buyers. A B2B business’ mailing list continues to be a valuable resource for marketing campaigns, and applying predictive analytics to already existing practices are easy. Email marketing allows you to control the message and understand exactly what content is going out at what time and to whom. Trying and testing different variations of your emails to consumers can provide insights into what works the best with your particular audience.
Email services often have their own software for testing capabilities. This software can tell you what worked best, and whether the different trends are meaningful or just a case of everyday variation. This is where predictive analysis comes in. Knowing what variations worked best in the past can influence your future emailing decisions and prevent you from using methods that do not work. The best results come from constant improvements and variation.
For a B2B business that strives to be successful in their digital selling strategies, predictive analytics are essential. While it can seem daunting dealing with large amounts of numbers and trying to make meaningful predictions before implementing the findings towards your marketing strategies, doing so will only improve the experience for your customer and your business.
Business intelligence tools are being implemented by B2B businesses to aid in their data analytics journey and are looking to increase in popularity over the coming years. Directive marketing can be a great induction into the predictive analytics era for start-ups. Every B2B company has email and the majority of email services have these testing capabilities already implemented.
The digital era of data and AI isn’t going anywhere. So, if you’re not already embracing it, take the plunge!