Customer Relationship Management systems (CRM systems for short), have significantly boosted the accuracy of keeping track of the number and quality of leads in B2B. Analytical CRM uses A.I. and machine learning to for example predict the likelihood of converting a lead, something that was previously a manual, laborious and inaccurate task.
This is the technical first part of a two part series on analytical CRM, have a read and stay locked to this space for part two.
Data analysis methods
Data analysis is the use of logic and reasoning to process every aspect of a set of data. In data analysis, the goal is to use analytical and statistical business analysis methods. Read on for all the aspects of data analysis.
Data analysis typically follows this process:
- after an initial stage where you agree to objectives,
- goals and metrics are found,
- data is collected, and
- data is cleaned (you correct and remove corrupt data to answer these questions: is the data accurate, complete, consistent and uniform?), and
- finally, the process is optimized for repetition. In other words: figure out how you can best do it again.
There are a few different types of data analysis. Get a quick and dirty breakdown of a few you should know in the following section.
Approaches to data
- In descriptive analysis you list and summarize data and this can for instance identify pain points, for example if there is a bug in your software that multiple participants commented on in a survey.
- Exploratory analysis explores the relationship between results. For example are people leaving your website quickly because your site loads very slowly.
- Confirmatory data analysis intends to uncover and confirm or disprove a hypothesis about a data set. So basically, is it true or false?
- Inferential analysis - predictions based on a (random) sample of the data. The random nature of the selection is done so the results are more accurate and bias is removed.
Examples of data analysis
One form of data analysis is text analysis, also known as data mining, where raw data is turned into business information. Large data is assessed for trends, patterns and relationships. An example is natural language processing, a technology used by us here at Ocean that is also behind voice assistants like Google Home and Alexa.
Big data analytics combines mining, predictive analytics, and machine learning on unstructured and semi-structured data. It can lead to better customer service and marketing, for example, depending on the data you uncover.
One type of data analysis you’ll want to get familiar with is clickstream analysis: how you map the potential buyer’s journey on your website to see who will be more likely to buy based on previous buyers’ webpage and purchase behavior.
The field of predictive analytics deals with using current as well as past data and models to predict future performance.
One business function of predictive analytics is it allows so-called ‘cross-selling’ to take place. An example of the concept is that you have perhaps bought a subscription to a type of software where you then receive a recommendation to buy an accompanying product from the same company. This is a classic cross-sell and is facilitated by another similar customer showing a high enough probability to purchase the accompanying product that it seems relevant to share with you as well.
Another valuable application of predictive analytics that you will also be familiar with is fraud detection. Banks can block credit card if their algorithms detect suspicious activity.
Customer retention is everything, preventing churn will boost your revenue in a more efficient way then working to gain new customers. With predictive analytics you can
- Identify churn patterns
- Create triggers for your Customer Success team to reach out to your customers when they are wavering
So if for example your E-commerce customers typically churn when they reach a certain revenue or when they change E-commerce platform from Magento to Shopify, you will know and have an opportunity to prevent the churn.
Traditional versus predictive lead scoring
In traditional lead scoring, you manually assign leads with a value. You give them a score based on your own evaluation and you repeat the process for each one of your leads. You could imagine that this could become a long process especially as you get more and more leads.
An example would be taking points off for a lead not providing a certain type of information, for example their full name. Giving points could occur for instance if you work in B2B and discover the lead did so as well. These bits of information would make the leads less or more likely to convert respectively. Other factors that could lead to the overall score could be age, gender, size of your company.
Predictive lead scoring uses an algorithm to predict which leads in your database are qualified or unqualified regarding conversion. It has advantages over the traditional method being on the one hand more accurate while also being less cumbersome. It can also evaluate factors like how valuable each lead is financially and sort them accordingly.
Predictive lead scoring can draw on: demographics, social information, behavioral data and technographics for example. Current and former customers are compared automatically to create a profile of a qualified lead. Technographics can help determine Total Addressable Market by seeing who could be in need of your B2B product.
With CRM intelligence the AI does the analysis and makes smart, automated suggestions. It understands language and language patterns that occur that are used for predictions. As was just mentioned, AI in a CRM facilitates processes like predictive lead scoring and also the process of how the leads were sorted out to begin with. This can help all of us bypass our unavoidable biases, save time on analysis as well as keep the sales force invigorated with more engaging tasks.
A great benefit within analytical CRM and machine learning is the option to look at sales forecasts and predictions. Having a statistically-backed prediction removes the standard manual processes that involve guessing from the equation.
Risk assessment is also possible with analytical CRM. You can scan through larger amounts of data making the process more efficient.
Research business analysis methods
Data analysis can be qualitative or quantitative.
Qualitative analysis deals with data from for example open-ended interview questions and surveys from the users of your B2B software.
While quantitative deals with a larger quantity of statistical data and numbers for example a great volume of yes/no questions in a questionnaire from the same users. The premise is as the name suggests rich, high quality data for qualitative versus greater quantities of data for quantitative.
Summary of the advantages of predictive marketing
A predictive marketing approach will benefit your business because it can be the key to maximizing conversion rates and return on investment (ROI) by identifying company segments to focus on. After a hypothesis is established, it can be tested on a continuous basis using the analytics gained from your marketing efforts.
With technology that facilitates predictive lead scoring, the integration of A.I. and CRM to name a few, there are great opportunities to optimize and make your marketing efforts more accurate and fruitful.
With a heavy topic like this, an overview like this one is hopefully helpful. For further reading and a chance to go into depth, check out the suggested reading list below.
Suggestions for further reading: