Has an Abundance of B2B Data Made Prospecting Effective?
Gurupandian Chandrasekaran is the Chief Product & Growth officer at Ampliz. He is a Growth-obsessed guy who has 12+ years of global experience in business/product strategy, entrepreneurship, sales & business development. He has expertise in Business Growth, Customer Acquisition & Funnel Development, Product Strategy, Enterprise Software (SaaS), Data Analytics & Customer Insights.
Guru has taken us back 25 years back explaining how the data industry was and he has made us travel from mid-nineties to present date providing the whole picture of the relationship between companies and customers. Interestingly, he started with Gary Thuerk, who sent the first email campaign promoting DEC machine in 1978 to 400 users and had $13 million in sales. Though this process led to a lot of deals, but it has created a distance. Hence, he named this process “The distance between a company and its customers”.
How has B2B sales & marketing evolved?
He has brilliantly split the B2B sales and marketing trends into three waves.
1st Wave – Online
He segmented 1st wave between mid-nineties to mid two thousand when people started visiting online and gathering tons of information. Companies went online, started their own websites, had a social presence, etc. All these increased in 10 years (the mid 90s to mid- 2000s). Customers had access to tons of content for the first time because no companies have websites. The started reading different contents, started having a social presence, etc. This enabled a huge flurry of activity in the first wave, but this lead to deal distance got a little bit bigger. Big data companies started helping during this phase by having a lot of contact directories and company firmographics details online.
2nd Wave – Inbound
2nd wave has been segmented between mid-2000s to early 2010 that led us to inbound way. This is the timeframe when the big events like HubSpot was started, the iPhone was launched, etc. So as a company you had an option to increase a lot of different content formats, video, audio, texts and infographics. There was a huge flurry of activity in automation. Emails, nurturing campaigns in social and other stuffs started being automated. Customers had the option to change the whole process. They made it like a buyer led journey. They wanted to be more informed, more educated and they wanted to learn on their own pace, not at the pace at which companies want. So this increased distance between company and customers little more.
3rd Wave – Noise
The 3rd wave that fell between 2011 to 2020 when we went from higher end 50 companies in sales marketing technologies to 8,000 which was a huge jump. Right now we’re in the middle of a crisis where we are rediscovering new roads as well. How is this affecting all of us in the B2B data segment? Because customers also going to leave a lot of digital footprint across different platforms. Though we didn’t have tech talk earlier, the footprint and the exhaust increased in this nice space and this cemetery, the next pillar in the distance.
It went from two blocks to five blocks and it kept increasing. Though B2B companies are trying to send you more data in different ways, the noise is not going away. We have lots of noise now. So what’s next?
What is 4th Wave?
What’s the fourth wave and more important question is that why has this distance increased between the company and the customers?
Every small and big company wanted to have more and more data, more quantity and quality though they don’t know to measure it. Every small and big company is using tons of tools to automate the whole marketing process, bombard people, and the distance between a customer and the company has just increased nonstop.
So the 4th wave is you need to have scale in a B2B data environment, you need to have personalization So you need data in the hundreds of millions at the same time. You need that personalization. Also, you also want the expert research. So your data partner should be both a combination of a platform, like a product or a data product like somebody who can tailor and give you intelligence more.
AMA with Guru
The following is excerpts from the AMA session with Guru.
What is the right frequency to data refresh to keep it in real-time?
That depends on your data set and the volume. If you look at 10 years back, you could say, my data is 10 years old or five years old and you can get away with it. Today, if you say your data is three months old, you cannot get away with it. Think about it right now. Right in how many layers of happened in the Coronavirus situation. People are looking at different places and their homes. So all the data you have at least 30 to 40% or even more is no longer valid. It’s kind of like you need to have access to near real-time data. So it keeps changing and with the environment and your company’s nature. it’s going to be an ever-evolving field.
Where do you put prospecting in the sales funnel?
I think today we are going into a mix of outbound and inbound. So prospecting is going to be a little lower compared to what the marketing team is doing. It depends on which team you’re talking about. If it’s an inside sales team, then you can sit very close to the marketing team and catch on it. For example, this is a marketing team doing an event and if you get a demo request, the inside sales team can jump on it immediately. That’s one use case. But if you don’t have that much of a marketing team and then your prospecting will be the first touchpoint of a customer. So it depends on how your team is structured and what your philosophies are.
In such a dynamic situation how do you evaluate the right data?
There has been a lot of literature in the last 20 years about we have the best data, we have the most accurate data. It’s very hard to test it at scale, but it’s very easy to test it at your level so you can just focus on what you need. Don’t care if your vendor has a million records, as long as you get your hundred leads records and just focus on those hundred tested and validated.
What is the right tool for prospecting apart from LinkedIn, which is more effective in terms of the quality of data?
LinkedIn is not the primary source of data because you’ve seen your own friends and colleagues who have not changed their LinkedIn profile. It might show that they’re still working in a previous job, but they have not worked there. So LinkedIn data is 50 to 60% of valid. So keeping that in mind, you should have the same bias to know like all data sets will have that kind of bias.
So again, it depends on your own small sample. You have to try different vendors to see what the right mix is. You can’t just choose a vendor who has a lot of records by saying he has already the scale, but you also need somebody to say I want more intelligence. Can you get this for me? So you want to mix of product and service.
I’m using multiple tools now to check data accuracy. I feel each tool is accurate with one data point. How do I choose one accuracy or abundancy?
I think both are irrelevant as long as your use case demands it. So if you are a small sales company, like if you have only one sales guy and he needs only 200 to 300 leads to work on every month. That is not abundant. So you have to figure out which is the right vendor who can give you the intelligence. The actual point of view should be about not quantity or quality, it is more about can this partner help me with the basic dataset but also involve me in the process to get final datasets.
Case Study aligned to this question:
So one of our customers asked us for data. We agreed to give a hundred thousand records based on his revenue industry title filter. I asked him a follow-up question, “Which hundred are you going to work on today?” So then this particular customer was thinking that which data point he needs to prioritize the first hundred and then he came up with some data points about a particular title, a particular team has to be working in three different remote locations and web traffic. So you have to have a mix of common data points and your own personal data points that are so important to your business. That’s the balance.
What is your opinion on data sharing economy? Will that be the future?
So data sharing is a very interesting aspect. We’ve already seen it earlier. If you remember there’s a company called Jigsaw 10 years back or even 15 years back that started, which said customers can come and upload whatever data they have and we’ll pay them credits they can use to redeem and get whatever data they want from our platform.
Eventually, it went on to get acquired by Salesforce, but it never went anywhere as a data company. It’s not about the availability of data that was a problem 10 years back. If you think everybody in this session would be saying, I don’t know how to watch movies online, but today the question is not about how to watch, it’s about what to watch because of availability of it. So data availability and abundance are not the problems. So data sharing will only evolve if you’re able to verify it at the source with people.
What are the latest trends in the data industry? I’m sure we have crossed more than just company and contact intelligence.
Of course yes, company and contact, we’ve definitely crossed. I think we have also crossed a little bit of proxy intent. It’s hard to get data sets. It’s the more personalized data sets and on one end you have the more niche personalized data sets. On the other end, you want to have a single source of truth. So it’s like a customer data platform. So I have all data coming in and I’m able to know which one is valid based on this particular platform is scrubbing it off, let’s say a billion records. Well, I have those two trends emerging at both extremes.
What would you predict the data decay % once the pandemic is over?
I think in most companies the data decay is going to be upwards of 40%.
What would be B2B 15 years from now in the data dimension? Any advancements and how do you cope with the change?
I think it depends on one big overriding sentiment about opting data. You have to see how big that trend is going to come. There are two trends. One is my opinion on data. Second is how am I going to get paid for my data? So those two trends could significantly impact how B2B companies use it. I think we can even combine B2B data with the referral. There are some angles that could possibly emerge depending on how open people are to sell their own data.
Can we use prospect’s data for inbound campaigns in some way?
Of course, yes. There are a lot of tools right now where you can use your prospect’s data map to the IP address. So when people come, you can start prospecting. But if these people are the same people coming to your website and checking it out, you can merge these two. That’s one way. The second way is even if you got an anonymous kind of a visitor, you can use that to immediately get data on what particular company or title this particular person is coming from.
How much data is enough data?
It depends on how many leads you want. So you want a hundred leads. Okay. So let’s say a conversion percentage, 20% of all customers are responding to your campaign. So you need 500 data points, 500 records. And again, see the thing is not about adding more rows, it’s about adding more columns.
So that is exactly what we say about, we use a term called contextual intelligence where we say, don’t just keep adding more rows because there are only 7 billion people in the world at one point. I want to end off it. Your target customers keep adding more columns because move away from revenue industry title, let’s focus on more data for you that will let you choose the first hundred or of the 200,000 records that are your prospects target market.
Is it better to buy data from data companies or to prepare your own, even if it takes more time?
I’m not sure how you’re going to make your own data. To compare that, is cooking your own food better than eating at a restaurant? It’s not. So there are benefits to both. The idea is one is time-consuming. You want to spend more time. If you’re going to cook your, if you’re going to prepare your own data, it’s going to take a lot of time for you to get to even 10,000 records. Instead of that, you just work with a vendor who’s trustworthy within a couple of minutes, your deal is done, you’re happy, they’re happy, and they’ve provided value. You provided money in exchange for that value.
How important are data enrichment tools?
I think a data enrichment tool is not an option anymore. It’s the necessity right now because you should move away from the more profile information or the company firmographic information. You need to add more data points. You need to understand the behavior of the proxy intent. You need to know what custom data points are so relevant for your business. So data enrichment is very key.
So this customer wanted the web traffic, they wanted a particular engineering title working and a particular title working in three different locations. This is gold to that particular customer. For the next customer, those three data points are completely junk data enrichment. It depends on your use case.
Will AI, IoT, and ML really authenticate the buyer behavior of a prospect in B2B sales?
It depends on how these technologies are embedded in our lives. So IoT is completely embedded into all the appliances they use. I think now we are going to have a lot of more videoconferencing, more real-time AI AR solutions at home. So these are going to validate. If I watch a video and it can automatically sense my interest and intent in the video itself or in the live conference, immediately, it’s going to help.
Let’s say this guy is going to have hundred zoom calls and immediately at the end of the hundred zoom demos, zoom is going to tell him these 30 people were more engaged in the remaining 60 using eyeball attention and AI mapping. It is definitely going to give you that extra column or data point for you to focus on those 20 instead of a hundred .