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:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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:
- 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.
- 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.
- 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.
- 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:
- 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:
- 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.
- 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:
- Number of Fans, Followers & Subscribers
- The social traffic, which includes the total visits, & the number of impressions viewed per visit & Click Through Rate
- The quality of interactions that the potential buyers indulge with on social websites including likes, comments, posts, tweets & impressions
- Measuring the worth of ad campaigns & whether they fetch enough revenue for the marketers
- Sentiment Analysis of the viewers across various social media platforms
- Defining & measuring conversion metrics for social traffic
- Measuring referral traffic by tracking various referring domains for social to web & vice-versa
- Measuring whether social traffic translates into web conversions & vice-versa
-
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).
- 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:
- Testing of usability:
Such types of tests are employed to test website design for its ease of use.
- 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.