Here start our first series of introduction with “B2B Analytics”. Before coming on B2B analytics let’s start with understanding B2B. In some countries, commonly Business-to-Business known as B2B or BtoB. It 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. It usually happens when:
- A business is resourcing material for the production process.
- A business requires services for operational reasons from another.
- Where business resale goods and services produced by others.
To understand its role in details, let us first get knowledge about what is analytics:
Predictive Analytics is a software using statistical models and machine learning helping in forecasting trends and outcomes based on the historical customer data either existing or similar. The goal of the predictive analytics is enabling the business in predicting leads and/or accounts which are expected to convert and/or have the biggest revenue impact. It is used by most of the business to analyze, describe, predict and enhance business execution for its excellence.
Profound changes in how businesses make purchase decisions, coupled with the sheer volume of information now available about those businesses, are driving strong demand for B2B analytics predictive marketing platforms. Nearly two-thirds of businesses have implemented predictive marketing analytics.
Major Potential is in Sales and Marketing:
More B2B marketers are relying on predictive marketing B2B analytics platforms to automate and streamline marketing and sales processes. Many vendors are rooted in lead scoring but have broadened their capabilities to include predictive modelling, personalization, and product recommendations that push deeper into the purchase funnel.
Major Applications Where B2B Analytics is Used in Enterprises:
Data Structuring: Data is the foundation of predictive analytics. Some vendors work primarily with structured data such as customer’s attributes and transactions. Others add unstructured data such as social media and website content.
Ideal Customer Identification: Prospect discovery is the process of using “ideal customer profiles” to find net-new prospects to add to the client’s marketing database. Predictive marketing analytics vendors use clients’ internal customer databases.
Business Lead Scoring: Lead scoring is the assigning of a value to each lead based on a predetermined set of rules or criteria. Predictive lead scoring can incorporate 200 or more data points by sourcing websites, social networks, and internal systems calculate scores.
Marketing Automation: Today’s predictive marketing analytics platforms offer native integration with many of the leading marketing automation platforms to draw from a single, integrated pool of customer data.
Cross-selling and Upselling Existing Customers: Marketing and sales staffs can utilize the intelligence provided by predictive models and lead scores.
Sub-Segments of B2B Analytics:
Finding New Leads and Lead Prioritisation: Marketers can use predictive modelling technology to sort through thousands of firmographics (i.e., company size, revenue, purchases) and signals (i.e., business expansion, new job posts, management changes) to determine which ones are actually good indicators of future behaviour. Some of the major competitors in this segment are 6Sense, EverString, Growth, etc.
Cross-Sell and Upsell: Companies can use B2B analytics to understand the characteristics of existing clients and the intelligence provided can be helpful in understanding. Companies working in this segment: 6Sense, EverString, Lattice Engines, Mintigo, Radius, etc.
Account Based Marketing: Analytics is used to make identity association features to understand client/company hierarchy system, within the company division, and purchase decision makers. This is helpful for initiating the sales talk with the key account. Companies working in this segment: GrowthIntel, Infer, Leadspace, SalesPredict, etc.
Personas and Customer Segment Building: This is the part which is used in selecting marketing approaches and/or product families, provide guidance in lead scoring and can be used in assigning leads to campaigns. The segment “persona” is mostly unvarying and “sales stage” or “engagement level” changes over the time. Companies: Lattice Engines, Leadspace, Mintigo, etc.
Sales Enablement: Intent data and alerts can also provide sales staff with the ability to be more proactive and timely with their outreach. Predictive models can help to identify the customers most at risk for not renewing their contracts. Companies in this segment: EverString, GrowthIntel, Mintigo, SalesPredict, etc.
What Kind of Companies need Analytics?
Some companies already started using Big Data and B2B analytics and those who are using their profitability and productivity rates are 5 – 6% higher in comparison to their peers. Companies are now turning the Big Data promise into reality.
How do Companies Take an Approach?
Use Analytics to Identify Valuable Opportunities: Analytics leaders take the time to develop “destination thinking,” which is writing down in simple sentences the business problems they want to solve or questions they want to be answered. These need to go beyond broad goals such as “increase wallet share” and get down to a level of specificity that is meaningful.
Analysing Client’s Behaviour: Today’s channel-surfing companies are comfortable using an array of devices, tools, and technologies to fulfil a task. B2B pre-purchase activities 35% B2B companies, eg. digital, should invest in websites which is the more effective way of communication nowadays.
Understanding New Target Markets: Big data has shifted the B2B marketing paradigm by enabling marketers selling to other businesses to learn more about their prospects and improving their ability to analyze the information. All of that data about target markets has given B2B analytics a chance to develop strategies.
Price Elasticity Measurement: Optimization is not just about using automation tools to make faster decisions. Price elasticity is the single most important factor, ahead of average selling prices, cost-plus margin targets or firm limits on price discounting authority, in setting profitable prices while keeping revenue risk to a minimum.
Note: To access the full information go through the whole report.
Top B2B Companies that are Using Analytics:
General Electric: General Electric has done a big investment in big data in recent times. The company is putting sensors on gas turbines, jet engines and other machines and connecting them to the cloud and analysing resulting data flow.
Maersk Line: Maersk Line operates in over 130 countries and owns more than 600 container vessels. The largest shipping company in the world transports goods with an estimated yearly value of $675 billion, where the company is using data analytics.
Intel: Intel’s sales organization contacted the IT division to help them optimize and improve sales. They wanted to be able to identify which of their customers had the greatest potential for increasing revenues. By using data analysis, they could create additional value by selling more to actual customers or selling to some accounts.
Lafarge: Lafarge is using GETPAID in the cloud as their primary tool for centralized, strategy-based collections. The solution integrates with their ERP, JD Edwards. It helps them apply collections strategies using built-in logic to prioritize individual collector queues.
FedEx: Efficiency and expediency is the lifeblood of FedEx, which handles nine million shipments a day and all the accompanying data. But FedEx isn’t just familiar with big data; it recognizes the potential behind it. The company recently decided to apply that data to physical items.
Cisco: Cisco is using predictive analytics for various purposes. Unlock more.
Mail Chimp: At MailChimp, the company uses predictive modelling across the application to improve the experiences of our users. Some examples:
- Predicting users who are unlikely to send spam, and allow them to begin sending email through the system.
- Predicting users who are likely to send spam, and shut them down before they send in order to protect.
- Predicting users who are on a free account but who are likely to pay in the future. Providing them with the same customer support given to current paid users, etc.
Pain Points for B2B Enterprises:
Connecting with Enterprises: The most difficult task for a B2B Enterprise is to know which is the right enterprise to collaborate with or to target the enterprise for sales.
Boosting Marketing Campaign ROI: The next task that is important for B2B enterprises is how to make a targeted marketing campaign and get the maximum ROI from it. If managers can reliably hone in on sub-segments of their market to improve the return on each marketing dollar spent.
Risk Management: Many organizations operating on a large scale, e.g. insurers or retailers, have a significant interest in predicting “critical events” such as a fraudulent claim of inventory overstock. These events are rare, difficult to forecast, and have outsized impacts on business bottom lines
Business Management: Many people read the magic of predictive analytics from the business perspective and watch perfect, actionable insights flow in and think that they just “turn it on”. In reality, it’s a constant, repetitive process, where data and business people must work closely together.
Access to Data and its Authenticity: There is a growing concern among the B2B marketers on to what extent they should rely on data that is provided by a third party. Analytics often requires fast and repetitive processing of full data sets. So, the B2B analytics companies need to develop strength with data points.
Lack of System Integration: System integration is defined as incorporating all the data into one system and getting hold of the analysis out of it. Many companies hire the third party for analytics and rely on them for all the analysis.
Lack of Alignment between Data and Marketing Segmentation: Traditionally, market segmentation has been defined as grouping your market into subgroups of people with the same characteristics. You could segment your market by gender, location, a favourite product, past purchases and so on. Today, it is much more effective to narrow your segments.
Current Analytics Companies’ Shortcomings:
Data analytics is the process of drawing inferences from large sets of data. These inferences help identify hidden patterns, customer preferences, trends, and more. To uncover these insights, big data analysts, often working for consulting agencies, use data mining, text mining, modelling, predictive analytics, and optimization. Although big data analytics is a remarkable tool that can help with business decisions, it does have its limitations.
Qualitative Research Integration: One of the major concerns for big data companies is a large amount of noise in data. So qualitative research includes open-ended interviews, focus groups and discussion forums. This helps in finding the major question points in any kind of analysis. Not many companies currently provide this feature to their clients and thus it can be a good strategy for company positioning.
Integrating Market Research with B2B Analytics: Many companies consider that B2B analytics and market research is one and the same thing. However, both of these are totally different segments and are used in isolation mostly. If a company can integrate market research with B2B analytics, then it can result in far better results which are actionable and can guarantee a positive impact. B2B analytics may result in several points which are not addressable and are not required. Using these findings, they may help in gaining an understanding of whether a company needs to introduce new lines of products or not and should it open new branches or not. Unlock for more.
It is no secret that most of the companies are dealing with predictive analytics or data analytics at one level or multiple. This is helping them to take big corporate decisions and taking them towards profitability. However, a big challenge is coming in the way of companies. There are lesser companies which are good in finance and accounting as well as in analytics. One of the biggest demands currently in B2B analytics are:
- Financial analysis
- Budgeting, planning and forecasting
- Operational analysis
- Cost management
- Customer Lifetime value
Running a B2B company has its challenges. Competition remains intense, profit margins are too thin and the top line has plateaued. How can B2B find secondary revenue sources? The answer lies in Business Intelligence (BI) within the part of data monetization. Data monetization the most dominant trend among all the B2B enterprises. And it’s no wonder why, given the vast amounts of data that companies now store. Finance is playing the crucial role now in enabling analytics and decision support.
Finance is the key to expanding analytics activities into areas that grow revenue and improve margins in their organization. In addition to core analytics activities like revenue management, tax analysis and investor relations, Finance leads the way in bringing previously unrealized value and growth potential to the organization.
The e-book “B2B Analytics and Its Scope in Industry Report 2018” is a practical roadmap for the businesses and startups. This e-book provides Customer Analytics, Sales Analytics and Marketing Analytics key trends for the readers. The e-book will also help the users to list the points where to concentrate more efforts in pursuance of the priority basis.
In this e-book we will explore:
I. Overview of Analytics in B2B Industry II. Use of B2B Analytics in Different Industries
Banks and Financial Institutions
Transportation & Logistic Industry
Natural Gas Distribution
Water Supplier & Irrigation
Electric Power Transmission
Media and Entertainment
Consumer Packaged Goods
Beauty and Wellness
We’ve created this e-book to guide the businesses to operate smoothly and cross all the hurdles related to the customer, sales and marketing. The e-book will help the companies and startups to reach and engage in new ways with the targeted audience.
For the full latest and updated B2B Analytics And Its Scope In Industry Report 2018, go here.
For customised market research reports for any kind of startups and business in any industry, you can contact Craft Driven Market Research team here directly.
Adobe, Automotive, B2B Analytics, Big-data, Cisco, Craft Driven, Craft Driven Market Research, CRM platform, Education, FedEx, General Electric, Healthcare, IBM, Intel, Lafarge, LinkedIn, MailChimp, Oracle, Predictive Analytics, Predictive Marketing Platforms, Salesforce