Predictive analytics in B2B marketing has always been considered to be useful. However, recent developments in AI and big data have turned it from just a useful tool to a necessary one.
Gone are the days when predictive analytics in B2B marketing only meant forecasting demand for a product or service. With the tools available today, it can do so much more such as scoring leads, predicting churn, and so much more.
What this means is that it gives an incredible advantage to any business with access to the right data. Inversely, any business without predictive analytics to support it is just fighting with one arm tied behind its back.
In this blog, we’ll list out exactly what edge predictive analytics can give to your business when it comes to B2B marketing. Before we begin, let’s take a moment to understand what predictive analytics is and how it works.
Understanding Predictive Analytics in B2B Marketing
Predictive consumer behavior has been the core objective of market research since the concept of market research was first introduced by Daniel Starch in his book Principles of Advertising in 1924.
This was followed by George Gallup establishing the foundations of qualitative market research. Finally, the Internet came about, introducing the idea of collecting massive amounts of consumer data from various sources.
All of this led to a new school of marketing based on predicting consumer behavior.
What is Predictive Analytics in Marketing?
Predictive analytics in marketing means analyzing historical data on consumer behavior and market trends to build a data-backed foundation for creating marketing strategies that anticipate the future.
The basic components of predictive analytics in marketing are quality data, meticulous analysis, and insightful interpretation of the data. All of these are fundamental to making accurate predictions that can help create successful marketing strategies.
Currently, the predictive analytics market is worth $17.3B, growing at a rate of 21.2% CAGR.
How Does Predictive Analytics in B2B Marketing Work?
Predictive analytics in B2B marketing is a complex process. It is both expensive and time-consuming but its importance and impact make it necessary. It is also a field of study with a wide scope.
A recent study of marketing executives showed that organizations are using predictive analytics in many different ways.
However, regardless of how it’s used, the fundamentals of how it works remain the same. For ease of understanding, we’ve divided it into these three stages:
Stage 1: Building a data-based foundation
The most important aspect of predictive analytics is the data. With so much of an individual’s actions being recorded online, it can be said that there’s more than enough data available to predict their actions and decisions.
The key is to get enough of the right data. A database with the data you need and a foundation to build your predictive analytics model is necessary to proceed further. These are the steps involved in building that foundation:
(i) Creating a Database
This is the very first process of predictive analytics once you have fixed your objectives and set parameters for the research population. Creating a database is a multi-step process involving:
- Data mining: Collecting data from various sources at scale.
- Data enrichment: Adding relevant information to collected data from additional sources.
- Data integration: Combining all the data into a unified format for easier analysis and interpretation.
Alternatively, you can purchase entire datasets to save all the time and effort required to collect and organize large amounts of data.
(ii) Analyzing the Data
Data in its raw form is just a lot of numbers. Analyzing it to identify patterns and understanding what they mean is the next step in building a foundation. Data analysis requires specialized knowledge, skillsets, and tools.
The two ways to analyze data include hiring your own data analysts or outsourcing the data analysis process to organizations with expertise in the area.
(iii) Interpreting the Data
This process flows naturally after analyzing the data and requires you to derive meaningful insights from the analyzed data and determine the significance of those insights.
This is one process of building the foundation that requires active participation from the business side, be it experts, managers, or executives.
Stage 2: Creating Predictive Models
Predictive modeling is the process of predicting outcomes of business decisions based on historical data. Predictive analytics in B2B marketing is the creation of B2B marketing strategies using these models.
In essence, predictive modeling can be used to determine the result of implementing marketing strategies, executing marketing campaigns, and similar actions. This can allow you to find the best course of action to get the optimum result required.
Until very recently, predictive models were only capable of estimating the demand for a particular product or service and predicting business outcomes to some degree.
However, technological development has led to a drastic evolution of predictive modeling. These developments include improvements in technology such as:
- Data mining which provided truly massive amounts of data for creating predictive models.
- Big data analytics which allowed more data to be analyzed for building predictive models.
- Machine learning that made the simultaneous building of multiple predictive models possible.
- Artificial Intelligence that can update predictive models automatically in the presence of new data.
In the present, hundreds and thousands of predictive models can be made in less time than a single one in the past. Predictive modeling has now become more efficient, accurate, and much more affordable.
This in turn has made predictive analytics more accessible to businesses, increasing its popularity.
Stage 3: Implementing Marketing Strategies
Predictive analytics in B2B marketing is ultimately a tool to make improved marketing strategies. Using that tool also requires specialized knowledge and skillsets.
Simply executing a marketing strategy just because the predictive model promised favorable outcomes is the exact opposite of what you should do at this stage.
Most experts agree on following these steps before making a final decision:
- Analyzing all facets of the selected model.
- Designing additional marketing strategies/campaigns that can bring the same or better results.
- A/B testing these strategies/campaigns with a real audience.
- Adding the results of the A/B tests to the database.
- Creating new predictive models.
- Repeating the above steps.
Once all variables are determined and contingencies made, you can finally execute your marketing strategy/campaign.
Additionally, analyzing the impact of your marketing strategy or campaign can give additional data to improve your predictive models and predictive analytics more accurate in the future.
Predictive analytics is a lengthy, continuous process that will keep using resources you can use to improve your business in other areas. So why use it? Because spending resources on predictive analytics in marketing is a net gain bringing many benefits to your business.
How Can Predictive Analytics in B2B Marketing Help Your Business?
Predictive analytics have always been known to be used for estimating demand and predicting trends. However, it can do so much more. Here’s an overview of some of them:
1. Make Strategic Decisions
Having access to predictive models can enable businesses to make strategic, data-backed decisions with a clear idea of the outcome. Predictive analytics can also help in making decisions that consider their long-term impact.
Decisions made using predictive analytics have a higher chance of achieving the desired outcomes while mitigating unforeseen risks.
2. Forecast Demand
Forecasting demand has always been one of the primary functions of predictive analytics in marketing. With the additional improvements in technology, it is even more accurate, taking minute aspects of consumer behavior into consideration.
Demand forecasting not only allows you to set achievable goals but also adjust resource spending and optimize marketing strategies.
3. Optimize Resource Allocation
A marketing budget should be fluid to achieve optimum efficiency. By estimating demand, predicting outcomes of business decisions, and identifying targets with the highest chance of conversion, businesses can allocate the right amount of resources to get the best results.
4. Segment the Market
Predictive analytics in B2B marketing can be used to segment the market accurately based on consumer behavior, demand, campaign outcome, or any other criteria.
Accurate segmentation can help create targeted marketing campaigns, optimize resources spent, improve the effectiveness of marketing strategies and increase ROI.
5. Improve Lead Quality
By analyzing purchasing history and other factors in consumer behavior, predictive analytics can enable you to not just identify leads with the highest chance of conversion, but also identify where they are in their customer journey.
You can use this data to assign a score to your leads based on their conversion chance, resources required for conversion, expected ROI, and any other factor you have data on.
6. Manage Sales Pipeline
Scoring leads using predictive analytics can enable your marketing team to prioritize them accordingly. This can optimize resource usage and convert leads faster.
This is very useful for businesses that require extensive communication between the sales team and the customer like the majority of B2B businesses. Predictive analytics can also help your sales team make preparations for the clients to push them along the funnel and make conversions.
7. Personalize Marketing Campaigns
Apart from scoring your leads, predictive analytics can help businesses identify the right channels and messaging to get the best outcome from particular leads.
Together, all of this can help in creating personalized marketing campaigns that deliver the most effective results.
This information can even be used to engage in Account-Based Marketing, picking high-value clients and optimizing the messaging for the highest chance of conversion.
8. Optimize Pricing
Pricing is one of the most important factors determining the success of marketing a product or service. By analyzing purchasing history, consumer behavior, pricing strategies of others in the industry, and how consumers reacted to them, it is possible to predict and set the right price to not just convert leads but also use the pricing as a competitive edge.
For individualized products and services or high-value clients in Account-Based Marketing, it can even be possible to set the optimum price for individual customers.
9. Enhance Messaging
Consumer behavior and preferences can be analyzed in depth to identify psychological, demographic, and other factors that they relate to. This can then be used to enhance the messaging your business can use to make conversions easier.
Not only that, but effective messaging can also build brand loyalty and foster long-term relationships, improving Customer Lifetime Value and customer retention.
10. Reduce Churn
Churn is when a customer decides to stop using your product or service, maybe even move on to your competitors. Predictive analytics is an excellent tool for identifying customers with the potential to churn, when they are expected to do so, and what factors are influencing their decision.
Furthermore, it’s possible to identify what factors can prevent their decision, how much resources to invest in retaining them as customers, and how it will impact the future ROI.
Predictive analytics in B2B marketing is a bona fide tool to take your marketing game to unimaginable levels. However, it is not without its own challenges.
What Are the Challenges to Implementing Predictive Analytics in B2B Marketing?
The rising number of businesses adopting predictive analytics in B2B marketing may seem like there are only positives to do so. But that’s not entirely true.
To make the best use of predictive analytics, it is necessary to understand what the challenges to adopting it are and how you can overcome them. Here are some of the common challenges to predictive analytics in marketing and how they can be overcome.
1. Cost
Predictive analytics in B2B marketing is expensive. That’s a fact. You would think that using improved technology to analyze data more efficiently and using machine learning to build predictive models would reduce the cost.
However, all of this requires vastly more data, powerful tools, and individuals with specialized skills. All of which can increase the cost associated with predictive analytics.
To a degree, this can be mitigated through outsourcing. However, developments in technology and the associated improvements to predictive analytics are more likely to keep raising the cost while making it impossible to stop using it to stay in competition.
2. Time
Collecting data, building predictive models, testing those models, etc., are additional steps that can delay the decision-making process.
Especially if you forego outsourcing and actively participate in the process, setting up predictive analytics can take months or years. Particularly so when it comes to data collection.
Unlike monetary costs, outsourcing can directly reduce or even eliminate the time spent on setting up predictive analytics. Purchasing datasets and Data as a Service solutions, as well as outsourcing the process to experts, are all viable ways to reduce this time.
3. Privacy
There are two aspects to when it comes to predictive analytics in B2B marketing:
- The privacy of the individuals whose data is being used.
- Confidential information of the business when the process is outsourced or AI is used.
Violation of the first instance can end up with legal repercussions, financial compensations, and even criminal indictment. Being careful about the source of data and compliance with GDPR, CCPA, and any other required regulation is an absolute necessity to avoid issues here.
The issue of protecting confidential information in the case of outsourcing and AI usage is not something limited to predictive analytics. There is no way of completely eliminating this issue.
However, partnering with reputable and reliable vendors, as well as having stringent measures of compensation in the contract, can do a lot to alleviate this risk.
Conclusion
Predictive analytics in B2B marketing is somewhat risky, very expensive, and slowly becoming a necessity for businesses to keep competing in any industry. Using predictive analytics will one day not become an optional choice.
Overcoming any challenges that limit its adoption can open the doors to many incredible opportunities. The biggest challenge when it comes to predictive analytics is utilizing these opportunities. Especially with the projected development of AI and machine learning, predictive analytics will keep evolving and bringing more benefits.
Setting up a roadmap for the adoption of predictive analytics right now can be incredibly helpful for your business. It will not only help you keep up with the competition but also put you in a position to take advantage of new developments and gain a competitive edge.