Enabling Demand Generation with Decisional Intelligence
April 20, 2021
As the need for a more streamlined B2B buying process grows, so does the requirement of data granularity in demand generation. Learning about your buyer’s specific needs and decision-making habits has never been more prevalent than it is today. The standard software buyer in 2021 is being pulled in multiple directions, resulting in a decline in attention spans. This directly affects your ability to generate demand and thought leadership, as you’re not only combating your numerous competitors, but also the buyer themselves. This creates an “us vs. them” mentality with your prospective customers, and that’s not a space any organization wants to be in.
In an effort to provide more information on how data is evolving in 2021 and what the growth-minded executive can do to manage and excel with their data, we interviewed Demand Science’s CDO, Shakeel Itoola. Shakeel has spent over 20 years working in growth-focused roles and using data to improve businesses and exceed goals.
Decisional Intelligence vs Big Data in Demand Generation?
As it stands, big data might be the most common form of processed data management. This comes from the simple need and/or thought of, “I have too much data to be managed manually”. Taking a step further, big data can then be processed and assessed through analytics to find consistencies and help streamline business practices. So, if big data is so commonly used and widely accepted, why are there other data collection and management solutions out there? The simple answer is- big data isn’t enough. As your needs as a business leader grows, so does the need for more accurate and intelligent data. That’s where decisional intelligence comes in. The ability to learn and influence data through behavioral and historical insights. Another form of decisional intelligence can be attributed to predictive analytics, or the gathering future insights on refined data.
Do predictive analytics and big data work together? Is there a difference between the two?
Predictive Analytics is a form of augmented analytics that uses Big Data, content and signals to answer the question “What is most likely to happen”? It applies a variety of techniques that range from simple rules-based analysis to complex Machine Learning algorithms to predict future business outcomes to allow fast and efficient decision making. Hence, Big Data and Predictive Analytics are interlinked and we, at DemandScience, apply a B2B lens to both to ensure the accuracy of our processing.
Collecting, Augmenting and Enriching Data
Now that we’ve covered the need for granularity in data, and the idea of gathering decisional intelligence, a reasonable question to ask is “what does this look like in motion?”. In a perfect world, you’d be able to gather the data needed for all corporate marketing initiatives and manipulate it to fit your specific needs at that time. However, the reality is that data you’re collecting often times comes in different forms, even if it’s the same “prospect”. The main reason for this is the sheer fact that leads change over time. You may acquire a large portion of data from either a paid or internal campaign, and over a short period of time, that same data will change priority and even become unqualified. Why is that? The short answer is the sheer fact that they were in a lower part of the buying funnel at the time of acquisition. The long answer is the need to augment and enrich your data as you acquire it. The value of a fresh database of prospects and customers is insurmountable. On top of that, it’s absolutely key to learn as much about your database as you can, in a timely manner. Why? Personalization. Leads are only truly qualified and unqualified when personalization is used in outreach across the organization. The only way to use personalization properly is if you’ve spent the time getting to know your data through data enrichment.
How is AI being used to improve Data collection and its quality?
AI has different meanings. In this specific context, DemandScience defines AI as Automated Integration and it is the use of innovative, scalable AI and ML algorithms to manage and process large volumes of data to make data management processes autonomous.
In the B2B industry in which we specialize, all data types are being collected, processed, and analyzed at different points in our Buyer Intelligence platform. The data formats, structure or lack thereof, source, frequency, recency, relevancy and quantity, vary across the spectrum. However, we use AI to organize, link, secure, understand and deduplicate it, among other things.
We have a strict “NO Garbage In, NO Garbage out policy” that gives us an industry leading performance metrics for data quality and accuracy.
What is the importance of fresh and/or recent data for lifecycle marketing?
First and foremost, I don’t believe that there is bad data, data is only imperfect. DemandScience has made significant investments in its Data Research team, computational infrastructure and AI processes to ensure that our data is recent, relevant and rich. Our augmented data is pro-actively served to our customers with guided recommendations on how to use this data, and when to initiate campaigns for maximum performance. We have also introduced customer feedback loops, data measures and generative AI innovations that automatically refresh our data.
We are a customer first company and providing fresh data to our customer is our top priority.
Understanding Your Target Audience
Yes, data enrichment and augmentation are important, but if you don’t know how to apply these activities to your target audience of buyers, then it’s all meaningless. That’s because, at the end of the day, being able to speak to your target buyers through demand generation is all that matters. You need to know what they want, how they communicate, where they gather information, etc. All of this information can be gathered through utilizing predictive analytics, intent, and decisional intelligence to collect and modify your data. However, you then need to take that information, and put it in terms that promote interactions with your prospective clients. That’s a lot easier said than done, but there are some key best practices you can follow to help you start dissecting your data and applying it to your content and marketing efforts.
- First and foremost, identify what your targeted buyer looks like. Then develop a plan to interact with them, and the people that influence the decision-making process.
- Segment your database by various metrics like geography, company size, industry, etc.
- Create personas that contain your targeted buyer, the database segments, and overall needs and challenges each persona might face. This will allow you to streamline your personalized outreach and focus on speaking relevantly to your customers.
How does personalizing data impact lifecycle marketing efforts and planning?
We are experiencing an accelerated digital transformation in our industry that centers on providing a unique and personalized experience to a buyer. Buyers want fast digital interactions, personalized content, trustworthy guided recommendations and predictable outcomes.
The industry is moving away from tactical selling and towards the Customer Lifecyle marketing where there is a significant emphasis on engaging with buyers earlier in their buying process, using digital and conversational channels to assist buyers, producing impactful content, predicting the performance of their campaigns, and providing personalized support with your CX team.
However, this means additional data endpoints and new predictive models to manage.
This sounds complex, how does DemandScience do this?
We have engineered an adaptive Data Fabric that allows us to collect, organize and link B2B data faster with no loss of information.
We use a combination of best practices (human-driven), process automations, AI data management, and ML models that are using a variety of intent, firmographic, economic, historical, and behavioral data to identify, score, nurture and segment opportunities based on a comprehensive set of data points versus name, location or LinkedIn profile.
Still, it is all about knowing who you customers are, identifying their needs, understanding their unique buying processes and engaging with them at the right moment.
Autonomous Intelligence (AI) for Demand Generation
As you begin to develop your growth plans involving personalized data from your target audience, it’s important to look at how you can continue to learn more about your customers even after they become a lead. Through AI and machine learning, marketing predictive analytics has changed how sales and marketing leaders manage demand generation data in their pipelines. You can now learn key behavioral trends from your prospects, and through machine learning, predict how they’re going to make a decision and when that decision will happen. Essentially ensuring you engage with the right leads at the right time. A mindset and challenge that was once fulfilled through intent data; predictive analytics has substantially increased the number of insights you can gather from one lead.
How does AI and enriched data benefit demand generation?
Our product portfolio uses smart data and AI in the demand generation process to identify the prospects with the right attributes; enrich recent data, augment matching and fitting, organize data per B2B verticals, predictively select best targeted accounts, and personalize content to improve conversion.
Our PurePredict product is being extended to include more decisional data points and we will soon release a new business outcome product that will be a major breakthrough for our industry.
Any final words?
My team and I are truly passionate about AI and data. Autonomous Intelligence is what I hope will be DemandScience’s contribution to the B2B industry. We use AI to shed a light on data to make it simple to easily compose lead generation applications and augmented analytics.
To conclude, we can see why expanding data capabilities through personalized and future insights is not only valuable but required for 2021 organic growth. It’s just not possible for a business to expand their solutions and promote year-over-year growth without proper data enrichment, augmentation, and an analytical understanding. Finally, it can’t be stated enough, but knowing how to communicate with your buyers is absolutely key, regardless of how it happens. At Demand Science, we not only offer the capabilities to generate high-quality demand generation leads that utilize predictive analytics, but we also focus heavily on the conversations and engagement you have with your clients. It’s important to us that a cohesive narrative is told so that each lead you receive can work their way through the funnel at an accelerated pace while also providing you with actionable insights. If you’d like to learn more, take a look at our solutions page here!