B2b big data

A sales situation is best described by a list of key performance questions together with their performance indicators (KPI). Once you have listed the most pressing sales issues and have determined their corresponding KPIs, the data available and the data analysis methodology should follow.

In Business-to-Business, the most valuable data of Big Data is always at hand: sales transactions from an ERP System and sales activities from a CRM Software. The reason is that to make useful predictions, sales managers mainly need to find “positive” cases: for example, customers that have bought a product or accepted an offer. In other words, Predictive Analytics is about describing the relationships between past data and predictive outcomes.

Using ERP and CRM sales data available and Big Data Analytics methods, Predictive Analytics can, for instance, help sales leaders to spot hidden opportunities within existing B2B customers. Do you need some examples or ideas on where to being? Here we provide three data mining techniques for predictive sales analytics based on ERP and CRM sales data.

Example Number One: doing B2B marketing segmentation with a clustering method

Successful market segmentation is the key to aligning services and products of a company with the needs and demand of the market. As Andre Lingenfelser wrote in Vertriebsmanager: „ Big Data methods open up an enormous potential to make segmentation much more efficient for a sales manager.“

A clustering method groups customers together by their similarity, usually not driven by any particular purpose. Due to the “positive” nature of the past sales transactions (customers that bought a product or service), to successfully undertake a cluster analysis there is probably no better start than a company’s ERP sales data.

Sales managers can use cluster analysis to group existing buyers into different sets or “clusters”. Once sales managers arranged customers in groups, they can compare trends in each one and dig for more sales potential.

Sales managers can compare customers clusters to discover cross-selling potential. Click To Tweet

For example, Qymatix Predictive Sales Analytics Software runs an advanced arithmetical clustering function to cluster customers together. It then detects buyers in those clusters that have not fulfilled the same buying potential as their peers. It then ranks them, helping to prioritise sales activities.

Example Number Two: using an apriori algorithm to develop a cross-selling strategy

Since the most popular ERP systems (SAP, Oracle, MS Dynamics, among others) use transactional databases, Apriori algorithms can offer valuable buying insights with low effort.

Like the clustering example above, an Apriori algorithm can spot associations and learn rules among customers. Marketers know Apriori well for its useful application in market basket analysis in B2C (business-to-consumer).

For example, if several customers bought products A and B together, the algorithm will cluster them into a set or basket. Sales managers can then compare these assortments to spot new business opportunities and increase cross-selling predictively.

Moreover, sales leaders can use these sets or clusters to detect pricing inconsistencies amongst customers. For example, such an ERP data mining technique can find customers consistently paying prices below a given average and spot them in advance.

Use Case: Predictive Analytics reduced customer churn while increasing sales team satisfaction and engagement.

Example Number Three: implementing a customer behaviour model for sales forecasting

With Big Data analytics, the right sales action should go to the right customer at the right time. How to improve sales forecast based on customer behaviour? Sales managers can apply predictive analytics models using customer response and then undertake appropriate actions.

An ARIMA (autoregressive integrated moving average) algorithm influenced by CRM sales data is an excellent Big Data method for improving sales forecast based on customer behaviour. Improvements in sales forecasting can offer benefits not only for stock replenishment but also advantages in customer loyalty, reduced attrition and churn.

An ARIMA algorithm mixed with CRM data can improve sales forecasting based on customer behaviour. Click To Tweet

For example, running an ARIMA algorithm across ERP sales transactions while feeding it with CRM signals (i.e. calls, complaints, sales activities) sales leaders can spot hidden buying opportunities and significantly improve sales forecasting. Likewise, using such an advanced predictive sales analytics function, sales leaders can avoid customer attrition and churn and recognise their reasons earlier.

Use Case: How cross- and up-selling based on Qymatix Algorithms is helping a medical components manufacturer to sell more.

CALCULATE NOW THE ROI OF QYMATIX PREDICTIVE SALES SOFTWARE

B2B Predictive Analytics examples using Big Data – Conclusion:

Regardless of how big Big Data is, the definition of data mining and predictive analytics methods begins with the understanding of the kind of information the sales force needs to be successful.

Mining CRM and ERP data for Predictive Analytics is a process intended to explore past sales data in search of patterns and behaviours among different variables. Once sales managers have discovered those relationships, they can use them as a model to make accurate forecasts, identify new sales opportunities, and increase the efficiency of the sales force.

In B2B sales situations, Big Data does not need to be big, it has to be useful. Click To Tweet

There are many inspiring examples of Big Data analytics. It is, however, critical understanding that in Business-to-Business sales, Big Data does not need to be big.

CRM and ERP Data Mining is a good start for Predictive Analytics. ERP and CRM sales data is one of the most valuable datasets a company can analyse. Therefore, sales managers in B2B should first dig this sales data for useful insights. Clustering analysis, apriori and behaviour-based forecasting are three good examples of where to start generating value from data.

 

How Incorporating Big Data & AI Can Renovate the B2B Industry

The ever-increasing use of the internet at every stage of B2B marketing leaves digital footprints everywhere. Big Data analysis systematically extracts & derives meaningful insights from a large set of data & is like a coded currency that needs to be decoded in order to fetch monitory benefits for the marketers.

Detangling the data deluge can help both the marketing & sales teams in B2B dominion to measure the effectiveness of their online campaigns, focus on the developing content that will drive greater conversions, focus on the engaged buyer persona as well as analyze how their marketing & sales efforts have been performing over a period of time. This is where Big Data & AI come into play.

Analysis of data not only helps define the trajectory of future sales but also helps to benchmark competition. Leveraging large data volumes helps B2B marketers in understanding & evaluating their performances, which, in turn, opens up a range of opportunities such as tapping into new market niches & appealing to a new buyer persona. Early businesses that learned & monetized Big Data Analytics for their businesses include giants such as Google, Yahoo & Facebook.  Nowadays open source BI tools have made data analysis convenient & cheaper for B2B marketers than ever before.

Analyzing metadata is easier & inexpensive than it has ever been with the help of open source Business Intelligence (BI) software tools such as Click Data, the ELK Stack, Jedox & KNIME, to name a few. Metadata exposes the possibilities for interdisciplinary data analysis & maintains the semantic elegance of the BI systems. Metadata is the key to turning the myriad of data into meaningful insights for B2B marketers & can be broadly classified into 3 types: descriptive, structural & administrative.

Each of the three types can be discovered, analyzed & monetized by the B2B marketers, in the following ways:

1) Descriptive metadata for SEO, content discovery & ideal buyer persona identification:
Leveraging descriptive metadata can be used to search & locate an object such as title, author, subjects, keywords, publishers, etc. This can help the B2B marketers analyze which keywords are best for utilization in their content strategy. This in turn also gives an idea of the major influencers within the industry creating insightful content & helps identify & segment the ideal buyer persona on the basis of their researching mannerisms as well as their level of engagement.

2) Structural metadata for defining & orchestrating steps sequentially for generating a greater Return on Investment (ROI) from Marketing & Sales endeavors:
Structural metadata analysis can give an insight on how to best leverage the small pieces of unstructured data from the marketing & sales departments to boost the ROI for the business. It defines the sequence of arrangement of unstructured data & orchestrates it according to the business objectives of a B2B organization to optimize the revenue. Structural data analysis can be used to bridge the gap between the endeavors of the marketing & sales team.
For example, if there are separate pieces of content created for generating marketing qualified leads, nurturing of leads & for generating sales qualified leads, then structured metadata analysis can help define the sequential alignment in which the contents have to be used to optimize the ROI for the businesses.

3) Administrative metadata to manage & preserve data sources in accordance with the Intellectual Property Rights (IPR):

Analysis of administrative data gives an insight as to when a business document was created, its source & the underlying technical information such as its file type. This can be of two types:

a) Right management metadata: This explains the intellectual property rights (IPRs) &

b)  Preservation metadata: This contains information about the preservation of a file source.

Metadata is vital for the B2B industry as it plays a crucial role in data warehousing, data mining, business intelligence, customer relationship management, enterprise application integration & knowledge management as has been depicted by Aleen Cho in his article on Semantic Web.

Clearly, predictive analytics is shaping the future of the B2B industry. Marketers need to utilize the data available publicly to analyze customer behavioral patterns & translate them into actionable revenue-generating insights, quickly & efficiently.  Optimizing customer experience for the buyer persona after their proper segmentation is a goal that automatically sets the pace for increased revenue for any organization.

As was predicted by Michal Brenner the future of B2B marketing will be aligned along with data, content & technology. Marketing automation today is capable of collecting data from several sources & converting it into actionable insights to improve customer experience based on the size of the custom social audience, their behavior, ad targeting, devices being used by the buyer persona for research viz. mobile, tablet or desktop.

How Big Data & AI stimulate B2B Marketing & Sales Endeavors to Optimize ROI

The intelligence driven by machines can be deployed to figure out buyers’ journeys & optimize the Return on Investment (ROI) for the marketers. The algorithms work the best if are based on real-time sales & marketing data & help in client acquisition as well as retention.

Following are some of the ways to leverage Big Data & AI for boosting the revenue of B2B businesses:

  1. Helps in generating new leads:

Using Big Data & AI organizations can filter through the leads collected from various sources such as browsing history; previous buying interests of the persona, clicks, etc. to generate a list of clients interested in their services. The quality of leads can then be analyzed in real time to derive the list of most engaged customers. Investing more time on engaged customers creates a better probability for conversions as well as helps to boost the revenue for the organizations.

  1. Helps in creating unique marketing campaigns:

The predictive analytics technique leverages the explicit & implicit data of the buying prospects including their social graph, online behavior, and content interactions along with their demographic & firmographic information to create targeted campaigns for the prospects with the help of several automation tools.

  1. Helps in guiding product pricing:

B2B marketers may be dealing with an array of products or services. But they aren’t the only ones in their marketing space to do so. In such a situation, proper analysis of the best price to deliver the product to the customers holds the key to the success of any marketing endeavor. The BI tools can help marketers with price analysis so that they can offer the best price to their customers.

  1. Helps to refine the market data for targeted marketing:

The B2B marketing endeavors rely on data from several sources, including demographic, firmographic & ethnographic data. By focusing on the set of buying prospects that are better engaged with the content & respond better to the marketing campaigns targeting them, the marketers can ensure that their marketing revenue isn’t wasted. Real-time psychographic analysis of the buyer persona can help in optimizing the ROI from the marketing endeavors.

  1. Helps in delving into the Intent Data for Intelligent business decisions:

A data-driven marketing strategy is imperative for marketers these days. The potential buyers look for tailored solutions to their problems. The intent data is an analysis of data from several sources to conclude whether a potential buyer is engaged with the product or services at offer & actually has a buying tendency.

The intent data is an amalgamation of data from third parties as well as the data reflected & analyzed on the website in real times as well as over a period of time. Basic intent data includes topics or keywords that your clients may be researching for, Ad Clicks, Social Media Participation, etc.

Studying Intent data helps the B2B companies in identifying & better appealing to their potential clients & in improving the sales’ conversion rate by shortening of the buyers’ cycle, opting for more targeted advertising & by winning the potential clients through multiple channels.

  1. Helps in personalized content creation:

Once the marketers are familiar with the researching habits of their buyer persona & understand their requirements & researching habits; they can focus on creating personalized content for them. The content strategy should be monitored at every stage of marketing & sales so as to bridge the gap between the content strategies of marketing & sales teams & increase the revenue for the B2B marketers. The goal can be achieved by using automated tools to measure the success of a content strategy.

Ways to Monetize Data Using Big Data & AI

The success of any B2B endeavor relies on understanding data on granular level & deriving meaningful insights to expedite the sales funnel & reach the ideal buyer persona so as to boost the Return on Investment (ROI).

Following are some of the ways to make data-driven decisions that can be used by B2B marketers to optimize their revenue:

  1. Meddling into the data:

Analyzing big data can give an insight into the ideal buyer persona, their research habits & requirements, economic situations & the quality of interactions they have with a B2B sales team. This, in turn, simplifies the task of personalizing the deliverable contents & improving the experiences for the customers. The customers can be segmented into several clusters based on their buying preferences & demographic, firmographic & technographic data & targeted more specifically.

  1. Employing automation for data analysis:

Investing in automated solutions for analyzing & interpreting data may actually be a wise decision for B2B marketers as it not only allows the marketers to keep a track record of the wide range of customers interested in their services but also allows to define pricing for each set of solutions that they have to offer, for a specific cluster of buyer persona. Providing customized solutions, in turn, will help in boosting the revenue of the organization as well in establishing brand equity.

Moreover, automation makes it easier to replicate & save data for future uses & interpretations. This may prove beneficial in deriving perennial & seasonal insights for the businesses based on trend analysis & real-time data analysis.

  1. Choosing a new & competitive pricing:

Keeping abreast with the market protectorate is important for B2B marketers, particularly when they decide to define the price for any of their products. The price should be competitive with the market or else the sales might get adversely impacted. A competitive price can be determined after benchmarking the competition & encompasses operational challenges, including training & up-skilling the sales representatives & instilling in them the confidence to negotiate terms with the buying prospects.

  1. Managing the performance of marketing & sales data:

Artificial intelligence makes it possible for marketers to measure the successes of their marketing & sales campaigns in terms of ROI & buyer persona engagement. Quantifying data provides the scope of improving the quality of campaigns.

The Qualitative & Quantitative Methods of Data Analysis:

  1. Quantitative Methods for Analyzing Data Extract:

Quantitative measurement of data & aligning it with the business KPIs is an important pre-requisite for the B2B marketers to boost their revenue in an era of digitized & data-driven decision making.

Following are some of the ways of quantitative analysis of Big Data:

  1. Managing web & mobile Data:

Measuring the number of viewers who use desktop & mobile to research about the products can give B2B marketers useful insights about the preferred platform for research by a specific cluster of potential clients.

Several other generic & specific discernments can be drawn by defining & analyzing metrics such as visitor activity, content usage, site usability, etc. Aligning the metrics with the KPI goals of a B2B company, after analyzing data on web & mobile platforms, can increase the ROI.

  1. Defining metrics for social media platforms:

As per their long & short-term business objectives, B2B marketers can deploy the metrics of social media. Social Media engagement & monetizing strategies work differently than web-based ones & hence measuring social media marketing endeavors requires an entirely different set of KPIs as follows:

  1. Number of Fans, Followers & Subscribers
  2. The social traffic, which includes the total visits, & the number of impressions viewed per visit & Click Through Rate
  3. The quality of interactions that the potential buyers indulge with on social websites  including likes, comments, posts, tweets & impressions
  4. Measuring the worth of ad campaigns & whether they fetch enough revenue for the marketers
  5. Sentiment Analysis of the viewers across various social media platforms
  6. Defining & measuring conversion metrics for social traffic
  7. Measuring referral traffic by tracking various referring domains for social to web & vice-versa
  8. Measuring whether social traffic translates into web conversions & vice-versa

 

  1. Website check for site quality & performance assessment:

Monitoring website is necessary for keeping a record of website loading time which impacts user experience, web-server performance (such as server availability & response time).

  1. Qualitative Methods for Analysis of Digital Data Footprint

The ever-evolving buyer persona leaves their digital foot trails everywhere in the process of researching for their buying preferences. A qualitative analysis of this data gives an insight into the buying intent of the prospects as well as also may be used to make sales projections.

Furthermore, personalized content for the buyer persona on the basis of their researching habits delivers a better customer experience which results in greater revenue for the marketers.

Algorithms can be used to analyze the psycho-graphical behavior of the buyer persona & even depict their mood while researching on the website. These algorithms provide a new avenue for engagement mapping of potential clients from which the B2B marketers can benefit.

Following are some of the methods of qualitative data analysis:

  1. Testing of usability:

Such types of tests are employed to test website design for its ease of use.

  1. Customer-sentiment analysis:

Realizing the requirements of the ideal buyer persona after discovering them, can be quite a task. The ever-evolving buyer persona is dynamic in their preferences too. Therefore, along with the automated ways of sentiment analysis, it is also advisable to interview a chunk of potential customers (ideal buyer persona) for better analysis of their requirements. Email & chat contacts & conducting online surveys are two other ways of customer sentiment analysis.

Precognitive marketing defines the course of action of B2B marketers these days as marketing & sales strategies are designed after analyzing data from various sources. Futuristic market discernments help the B2B marketers to strategize & achieve greater ROI.

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Transform your B2B sales by Leveraging the Power of Predictive analytics and Big Data

Transform B2B Sales with Predictive Analytics and Big Data

Predictive analytics have been playing a dominant role in the modern B2B sales by delivering significant reforms in the accelerated IT landscape and providing a massive scale for growth, efficiency, and targeted online marketing. More and more B2B companies are leveraging Big Data and predictive analytics to provide value to their customers and tread ahead of the competition.

 

Big Data & Predictive analytics can reform your business identity by focusing on the commercialization, cost-efficiency, capacity, and coverage of your sales initiatives. Predictive Analytics in business, allows you to automate certain market responses in a given customer scenario. And Big Data enables you to assess the interaction level with your customers at an exceptionally intimate and minute level. Using the two you can map your customers’ journey, identify prospect customers, and garner more leads.

So, how have you been leading your B2B sales initiative?

Data is Zeros and Ones – Ergo, Pave it Accordingly to Start Pitch-Perfect Customer Journey

When Pat Gelsinger, CEO of VMware said – “Data is the new science. Big Data holds the answer,” he simply corroborated companies’ dependency on Big data. In case of sampling a subject of interest, the more samples one has; the better is the result. Even the predictive analytics on large data is more accurate and help discover patterns. Hence, Big data and analytics connote competitive advantage.

However, how B2B marketers make use of big data is the impending question. And how Big data can transform B2B sales and in what way? Here are a few insights for the growing fixation with realities of Big Data for B2B –

  1. Data Asymmetry

    Since the astute observation by Gordon Moore, also hypothesized as Moore’s law, the storage device capacity has been increasing every year. It is getting cheaper as well. Thus, accumulating the data is not important, but what will marketers do with all this data?

    “What gets measured, gets managed,” – true. However, only 24% of the customers feel that their companies know them. There is a serious gap between what they want and what the B2B sales provide. Some of the looming questions for all the B2B professionals to answer are –

    • How to switch digital from a destination to an insight and leverage Big data during the process?
    • How to cut or remove the combative animosity between marketing and technology to give best to the customers?
    • And how to leverage big data to drive marketing strategy?

    You should, not only work as per the demands of your customers, but also know what they will need in the future. A Big yes to the Big data. And with the right collation of data, you can also decide on the levels of engagement with your customers and understand who they are (personas), and what they want (sales pitch).

  2. Predictive Marketing Analytics using Big Data

    The future of every industry lingers around data. And Big data and business analytics are expected to see an annual surge of 11.7%, which will cross the $200 billion mark by the year 2020. You too can transform your B2B sales by leveraging the power of predictive analytics and Big Data. Here is how –

    • Leveraging Data, Algorithms, & Machine LearningData – collated and accurate, is today’s gold. Big data helps in the collation and predictive analytics aids in improving the accuracy of the collated data. Thus, boosting the overall B2B sales’ efficiency. A few things that you can achieve by the trio – Data, Algorithms, & Machine Learning are –
      • Improve the accuracy of lead generation and automate presales processes
      • Generate a comprehensive portfolio of your customer by combining the internal data sources, which includes customers’ previous history and external data – social-media, etc.
      • Segregate the leads in terms of importance
      • Identify more opportunities and convert them by intelligence automation through machine learning tools
      • Rely on AI-enabled agents that utilize natural-language processing (NLP) to automate initial lead-generation activities, including managing basic questions, offering free trials, and automating initial presales questions

      Remember that data play an important role in transforming a business. Beneath that, you can find something that works in disguise, i.e., Predictive analytics. That is why Predictive analytics & B2B sales is a match made in heaven.

      So, what is predictive analytics and why does it matter? – “Predictive analytics is, simply leveraging the data, statistical algorithms and machine learning to categorize, and recognize the probability of future outcomes.” It can help you understand the forthcoming trends, market drifts, and your buyers’ moods if you know how to analyze the data. That is why experts from all sectors are pushing for inculcating predictive analytics for B2B sales.

    • Adopting Best-in-class Technologies & Cloud-based ServicesBig data and predictive analytics have opened the floodgates to innovative technologies and cloud-based services. It helps to –
      • Maximize the data management strategies
      • Adopt strong data governance policies
      • Have better data representations
      • Provide easy and guided data systems
      • Restrict data from unauthorized access, and many more

      According to a study by the International Data Group, 69% of businesses have adopted the cloud technology in one capacity or another, and 18% are planning to implement cloud-computing solutions in the future. And if you leverage the cloud systems & tools, your sales staff can conveniently access the information whenever and wherever they need.

  3. Combination of Data Sets – Whole Is Greater Than the Sum of Its Parts

    If one has several data sets, the predictions will be more accurate. And knowing how and where to blend in and break the data, divulges critical behavioral insights.

    Some instinctual examples –

    • If you try and understand the predictive analytics through the example of the travel industry, you will see that they set flight paths and leverage predictive analytics to offer the best ticket rates as per the season
    • Or, in the case of the banking sector, they utilize Big data and predictive analytics to manage risk and detect fraud
    • One recent example is the data breach controversy related to the much adored and respected organization – Facebook. The data collected by the Cambridge Analytica were used for psychographic analysis to influence voters in the USA’s presidential election campaign

    As a B2B marketer, the Big data analytics tell a lot about your customers’ behavior. You can offer them an idea about your services or products that would complement their existing solutions.

Predictive Analytics in B2B Sales – The Right Data Management Strategy

With economy sharing, globalization, and other technology reforms in the data analytics field, the stretch of predictive analytics is limitless. Some of the right B2B sales strategies would be –

Utilizing Predictive Analytics in eCommerce for B2B Sales

1. Utilizing Predictive Analytics in eCommerce for B2B Sales
In the coming years, human intervention will be minimal, that means, if you do not adopt automated tools and digitize your web-store, you will be left behind. Predictive analytics can accurately tell which customers to contact, their buying behavior, and which other domains to tread.
According to a report by BusinessWire, 90% of B2B merchants expect e-commerce sales to increase by the end of 2018. Thus, you should also use the predictive analytics tools or collaborate with an expert who can build you one. A few benefits are –

  • You can successfully know in advance what your customers are most likely to buy and hence provide prescriptive products and place your content likewise
  • You can, to a great extent, determine the highest price a customer will agree to pay
  • You can initiate target recommendations, promotions, and plan a better price management strategy
  • You can improve your supply chain management, warehouse management, and logistics
Artificial Intelligence to Boost B2B Sales

2. Artificial Intelligence to Boost B2B Sales
Given its potential for marketing and sales, Artificial intelligence has transformed the way customers interact with brands and leverage their services. And to successfully achieve that, B2B’ ecosystem space must be filled with concentrated efforts towards getting insights on their customers.
Users leave individual marks when they browse through the web, communicate with brands using different channels, and take part in the research. Thus, these data can be constructively used to analyze their mindsets, demographics, personas and buying behavior.
But, the prime question, if you lack the expertise, is how to make a precise interpretation of all the data that you collect?
The answer is collaborating with an expert who can bring together the best of Big Data, Predictive Artificial, and Artificial Intelligence.
Artificial Intelligence, inherently surpasses the human ability to accurately emulate the space around which it is leveraged. Coupled with the AI-enabled computing system and predictive analytics, it can harvest and process the data and reinvent your B2B sales process. You can rely on data visualization tools, like Tableau, Clearbit, etc., for transforming the way you consolidate and present your data.

Customized Dashboard for Your Niche Requirements

3. Customized Dashboard for Your Niche Requirements
If you have a customized dashboard according to your business requirements, you will have an upper hand in managing your data. Predictive analytics tools like RapidMiner Studio, SAP Predictive Analytics, etc. can give you the power to manage your data seamlessly. The power of visual representation is unmatchable, and you do not have to sit with excel sheets and try to understand the hordes of numbers. These tools can consolidate and present the data in the manner you deem fit and you can share the data in a way that is easy to understand by everyone. A win-win for all.

Today, most of the organizations, irrespective of their areas of expertise, are more and more relying on predictive B2B sales analytics to create value out of sales data. Thus, empowering your sales team critical to driving the best digital strategy to woo your customers. And customers do not come easily, you have to express your business’s visions in the way your customers want. It does not mean you need to change your business’s value propositions rather adopt methods, technologies and tools to express it better – by using Big data and predictive analytics.

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Nowadays, people produce a large amount of data compared to previous decades. We have generated 90% of the world’s data in just the past two years. Businesses, including B2B, leverage such a vast amount of information to receive valuable data-driven insights.

To keep up with competitors can use their digital resource planning (ERP) and customer relationships (CRM) systems to analyze the data about previous and current customers. Then, they can apply such insights in all the stages of the B2B sales funnel, from marketing to calculating customer lifetime value. To achieve this goal, you need to understand what is predictive analytics and how B2B companies can leverage this technique to achieve an increase in revenue.

 

What is data analytics?

Predictive analytics seems quite simple on the surface – this is a process of analyzing data received from past events to predict outcomes of future events. Still, the process itself is more comprehensive than that. To make accurate predictions, you need to gather relevant data that may vary in size from different sources.

The image below demonstrates the detailed the process of Data Analytics.

data analytics in 5 stepsIn the B2B sector, data analytics required gathering and analyzing Big data from all the previous clients, which required a powerful software.

B2B companies can use the following information about their clients:

  • Content preferences
  • Interactions with certain content
  • Use of certain features in the applications
  • Search requests
  • Browsing activity
  • Online purchases

After specialists gathered raw data, they need to analyze it and create a bigger picture of how one or another business can improve business operations. Now, let’s find out how data analytics can improve B2B sales.

 

Why Does Data Analytics Matter for B2B Sales?

By applying big data analytics, statistical algorithms, and machine learning techniques, B2B businesses can identify the likelihood of future outcomes by considering historical data. The recent research among B2B sales companies showed that among 1000 companies, 53% uses big-data analytics for adding new services, providing better value to their clients, and delivering their services in a better way.

In this way, B2B companies can deliver more flexible and high-quality products. Thus, they can bright more value to their clients.

With this in mind, let us take a look at how data analytics is boosting sales in the B2B sector.

 

Data analytics in B2B sales: essential use cases

Below we have gathered the main use cases of how B2B companies apply Data analytics to improve their sales funnel.

 

Improve marketing segmentation

B2B companies leverage Big data in Customer Analytics at the very beginning of sales funnel, i.e., during market segmentation. Since using Big Data, sales managers can make a more clear market segmentation. To create ideal buyer personas for narrower targeted marketing, sales managers gather ERP sales data and undertake data analytics for grouping up existing clients into different target personas. By using ready-made targeting personas, businesses conduct more specific targeted marketing, while reducing inefficiencies.

 

Effective lead scoring

Data Analytics allows B2B businesses to use historical sales information about previous clients to score new business leads most effectively. To predict which business lead is the most likely to close the deal, companies develop lead-scoring algorithms in combination with external data. Such an approach results in a 30% higher conversion rate for B2B companies across different industries.

 

Better sales forecasting

One more use case of how Data Analysis in B2B sales can increase revenue is sales forecasting, which is considered to be a procedure is of vital importance. Thanks to precise sales forecasting, a business can smoothly move through the financial year. On the other hand, unclear sales forecasting results in reduced process efficiency, as well as resource allocations.

To create a precise sales forecasting, specialists leverage CRM sale data (customer responses, customer behavior, complaints, sales activities) and autoregressive integrated moving average (ARIMA) algorithm. Thanks to this strategy, businesses receive insights about hidden buying opportunities.

 

Accurate product recommendation

As a rule, B2B businesses could not show clients a traditional portfolio. For that reason, it might be hard to offer one or another client the most suitable services or products. This fact also results in time-consuming integrations with clients and missed opportunities. In this case, sales managers can leverage Data Analytics algorithms to divide customers into different groups based on their needs. Then, salespeople can compare such customer groups with their previous clients and suggest them more relevant products or services. Also, such an approach, based on the Data Analytics, could identify hidden cross-selling opportunities, thus, increase revenue.

 

Transparent customer lifetime value

For B2B salespersons, accurate calculation of customer lifetime value is always valuable. Since the company profit correlates with relations with customers and not all sales representatives, know when such relationships might end. To get more precise forecasting of CLV, B2B employees can use Data Analytics, in particular, predictive analytics methods and techniques. In this way, salespersons can calculate for a long one, or another customer will work with the company on the base of one’s profile and data from previous interactions with customers that had fallen into the same group.

Conclusion

Data Analytics is a process of analyzing business information to receive valuable insights. As for B2B companies, this is a useful tool for their income. By using information about previous clients kept in CRM and ERP, B2B businesses can apply clustering analysis, behavioral-based forecasting, and predictive analysis for:

  • Developing a marketing persona for each target customer, thus make ads and marketing activities for effective
  • Scoring new leads using lead-scoring algorithms based on the interaction with clients from the same target group
  • Forecasting revenue based on customers activities and autoregressive integrated moving average, thus achieve more financial stability
  • Providing more relevant services and products based on closed deals with similar clients
  • Estimating customer lifetime value using predictive analytics methods to retain customers more efficiently.

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6 Reasons the Cloud Matters to B2B Companies

The advantages of cloud computing are frequently discussed but hold little power unless evaluated from a personal point of view. That means B2B companies need to stop analyzing cloud solutions from a B2C perspective. Instead, by looking at the capabilities and advantages from a B2B viewpoint, you can truly understand the capabilities of these technologies.

B2B IT Decision Makers: Here are 6 Reasons to Invest in the Cloud

As a business owner or IT decision maker, there are dozens of reasons to consider an investment in the cloud. Specifically, you’ll want to focus on the following six reasons:

  • Ability to effortlessly scale. B2B companies scale at a much more drastic pace than most B2C businesses. That’s why it’s critical to only use resources and tools that grow and contract with you. Because most cloud solutions operate on a subscription basis, you’re able to change your requirements at virtually any point in time. Additionally, because you can increase output without adding more people, you can reduce your cost per unit and achieve economies of scale much quicker.
  • Access software from anywhere. With traditional software, you have to physically be at your desk or computer to access your company’s resources. With cloud solutions, though, you can use them from any device with a network connection. That’s why millions of B2B companies are investing in cloud accounting software, online payroll management tools, and other similar resources.
  • Less upfront cost. One of the biggest benefits of cloud computing is the upfront cost savings. Instead of spending thousands of dollars on IT infrastructure, you can allocate that money to critical tasks and pay as you go. It provides enormous financial flexibility for B2B organizations that find themselves strapped for cash.
  • Quick disaster recovery. With cloud technology, disasters suddenly become less cumbersome and, well, disastrous. According to research conducted by the Aberdeen Group, the average downtime for a cloud user is as much as four times less than a similar business without the cloud.
  • Effortless collaboration. Did you know that as many as 73 percent of knowledge workers regularly collaborate with people in different geographical locations and time zones on a monthly basis, according to an Adobe Acrobat white paper? That means nearly three out of four people have to send back and forth messages, reformat files, and account for time differences when working on projects. While cloud computing doesn’t solve all of these issues, it certainly makes collaboration much easier.
  • Smoother mergers and acquisitions. Ultimately, cloud computing allows for smoother mergers and acquisitions, should your business ever decide to go that route. This is a result of data being readily available and consolidated – as opposed to B2B organizations in which files are spread out over multiple servers and devices.

Cloudy With a High Probability of Success

There’s very little inherent risk in making a move towards cloud computing. The upfront cost is low, it is able to scale as you grow, it protects your valuable data and files, it makes it easy for everyone to communicate, it is accessible from virtually anywhere and streamlines organizational changes and fluctuations. Next time your team discusses IT changes, make sure an investment in the cloud is the first topic on the agenda.