4 ways sales intelligence data can accelerate your pipeline
By offering real-time data insights into buyer journeys, sales intelligence can be the key to accelerating your pipeline and closing more deals.
As anyone involved with selling B2B knows, the B2B sales cycle is a marathon, not a sprint.
The buying decision is spread out over months and involves a bunch of stakeholders: be they the eventual user(s), the decision-markers, legal teams, tech teams, etc. In one way or another, they all need to ‘buy in’ to the product and engage with your brand.
But resting on your laurels as accounts trudge their way down the pipeline simply because ‘it takes long’ is a poor (and risky) race plan.
Elite marathon runners don’t. run. slowly. In fact, they hold a pace of around 20 km/h over the 42km - a sprint to you and me. This, of course, takes hours of training, and above all, becoming an expert at reading the signals your body is sending out throughout the race.
It’s this same instinct that B2B marketers and sales reps need to harness when pushing their marathon buyer journeys to the limits. Although in this case, it’s customer data signals that help get accounts over the line.
In this post, we’re going to cover how to find and read these vital data signals that help B2B commercial teams keep the velocity on their pipeline.
We’re answering the following questions:
What is Sales Intelligence?
How do you collect sales intelligence data?
Why is sales intelligence data important to B2B commercial teams?
4 ways you can use sales intelligence data to accelerate your pipeline.
What is Sales Intelligence?
Definition: Sales Intelligence is the process of collecting and analysing customer data to improve lead generation, prospecting, and lead nurturing, and ultimately, close more deals.
Customer data, in its simplest form - name, employee count, funding/revenue, job titles, even email addresses - is not that difficult to find. But this data does little to bring clarity to the long and complex B2B sales cycles.
It is here where sales intelligence comes into its own.
Sales intelligence data offers customer insights at a much deeper level. These insights include:
Length of buyer journeys - tracking the time-to-revenue metric to know how long customers take from the anonymous first touch to the last (and the length of each pipeline stage)
Demand data (first-party intent and engagement data) - tracking every event/touchpoint a customer has with your brand and when.
Source of acquisition - identifying what channel started the account’s journey.
Enriched customer profiles - details on the customer account including personas involved in the process, tech stack, industry, size, etc.
This enables sales reps and marketers to know what leads and customers are doing as they move through the pipeline: who’s involved in the buying process, what are they reading, are they visiting review sites, do they match your data-proven ideal customer, etc. More on this in the final section below.
In this way, sales intelligence fundamentally deals with B2B commercial teams’ struggle to make sense of the increasingly complex B2B customer journey.
How do you collect sales intelligence data?
It’s clear that these data insights come from a range of sources, especially as we’re dealing with accounts of multiple stakeholders. Where, for example, one stakeholder clicks the Google ad and reads a blog post, while another subscribes to your email newsletter, and a third is also present at a demo meeting.
The problem, from a data point of view, is that each of these sit in different tools, Google ads, email automation software, web tracking data sitting in a data warehouse, CRM. Worst yet, the data is siloed within each of these.
It’s this challenge that has seen a rise in Sales Intelligence software such as Cogism, Vainu, Clearbit. But other tools with robust tracking and data processing, such as a multi-touch attribution tool, also achieve the same results.
The bottom line is that whatever setup you have, it needs to be able to:
Track on-site customer behaviour.
Pull customer data from across the B2B go-to-market tech stacks - think all the tools used by Sales, Marketing and CS.
Transform and process all this data so that it’s unified and can be easily visualised.
Ensure that this process is dynamic, meaning that data is continuously gathered and processed to offer up-to-date insights in real-time - to ensure you can pick up those signals as soon as they show!
Check out this post on activating Demand Data for Sales teams.
Ok, so now we know what sales intelligence data is and how you can get hold of it, let’s now take a look at why it matters.
Why is Sales Intelligence important?
As we’ve already mentioned, the B2B customer journey is long and complex. This complexity makes it tougher for B2B commercial teams to sort the wheat from the chaff when it comes to acquiring and nurturing leads.
Sales reps in particular have to juggle a multitude of accounts at various stages of buying and showing different signs of intent. Add to that the challenges of identifying ideal customers when prospecting, and you’re left with the high risk of wasting time on poor leads and unproductive prospecting.
The result is a slow pipeline and lower close rates.
Without adequate data tracking and processing in place, the crucial data signals that open up the customer journey are not being picked up. For too long Sales Reps and Account Executives have been relying solely on email responses, read receipts, or weak IP-look up signals for assessing how active an account is.
By providing data insights into how and when accounts are interacting with your brand over the course of the buying cycle, sales intelligence data offers these actionable insights.
With this, both Marketing and Sales teams are able to better optimise their efforts to zero in on hot prospects and nurture leads through the pipeline.
To return to our marathon runner analogy, sales intelligence data are those signals which allow B2B marketers and sales reps to accelerate velocity in the pipeline.
Let’s get your pace up then, shall we?
4 ways you can use Sales Intelligence to accelerate your pipeline
Now let’s get to the juicy part: operationalising your sales intelligence data. That is, how can you use this data to accelerate your pipeline and close more deals?
Let’s remind ourselves of the four main data insights sales intelligence offers:
Length of buyer journeys - tracking the time-to-revenue metric to know how long customers take from the anonymous first touch to the last (and the length of each pipeline stage)
Demand data (first-party intent and engagement data) - tracking every event/touchpoint a customer has with your brand and when. You can learn more about intent data in this post.
Source of acquisition - identifying what channel started the account’s journey.
Enriched customer profiles - details on the customer account including: personas involved in the process, tech stack, industry, size, etc.
1. Know your ideal customer inside out
First on the list is using historical data to identify what your ‘Ideal Customer Profile’ (ICP) looks like.
There’s no beating around the bush here, nailing your ICP is a make or break for efficiently selling a product. After all, virtually all your activities rest on targeting the right persona at the right time. Fail to get this right and you’ll spend copious amounts of time on dead-end leads.
Getting it right requires quality customer data. By collecting data from across the B2B go-to-market tech stack, sales intelligence builds rich account (and user) profiles, enabling a data-driven ICP process.
What’s more, because the data is dynamic, you’ll always have the most up-to-date criteria of your ideal customer profile available.
With this, you’ll be able to ascertain what your most successful recent customers look like: including industry, size and revenue, tech stack, and the job position your champions have.
Dreamdata offers dynamic data on all New Bizz deals, leads in the pipeline and individual contacts.
2. Qualifying and prioritising leads
Even with your ICP on point, you still need to qualify and prioritise your leads to ensure you’re using maximum effort on those that are most likely to close the soonest.
First you need to confirm that the lead fully matches the ICP, if it doesn’t then it needs to be disqualified asap. Any time spent entertaining misfits is time not spent on leads that are most likely to buy.
With that said, it’s worth exercising a small margin of error on the ICP, especially if your customer base isn’t huge. Meaning, that the lead might not fully match the ICP but is there about and might still buy the product.
Next, you need to prioritise. Here there are two interrelated signals you want to be paying attention to:
Time spent on each pipeline stage.
Lead activity.
Knowing what your account is up to and when also means being able to determine when to cut a lead loose.
With your average time-to-revenue metric in hand, you can benchmark whether or not a lead has enough velocity, and whether it needs a nudge, de-prioritising, or closed-lost.
For example, say your customers spend on average 30 days between MQL and SQL stage. If a lead goes considerably beyond this time you need to action accordingly.
The same logic is true with activity levels. When you’re tracking events across the customer journey, you’re able to compare activity levels of successful customers with those on the pipeline. Are they having as many touches (showing similar levels of intent) as those successful customers?
3. Timing and personalising your outreach
You can time your outreach perfectly. Instead of the spray and pray approach Sales Rep’s rely on when working only with their CRM, the Customer Journey dashboard shows exactly when a touch has taken place and what that touch was. Meaning you can time your outreach to land after particular activity.
If a lead isn’t reacting to your (perfectly timed) outreach and has gone beyond the average time on the stage, you know they’re slowing down your pipeline velocity and are ready to be closed as lost.
Personalised messaging
Going into conversations without a clear picture of how the account came through the funnel and what they’ve interacted with can make it very difficult to adequately tailor the conversation - whether this is an email or a demo call.
Once you’ve revealed what your lead has been up to, you can mould the discussion accordingly. Making the most out of the benefits.
Let’s use a hypothetical example. A number of contacts within an account may have read a blog post on one of your product’s features. They also attended a webinar on the same topic and looked at the same product feature page on your site.
Not only is this feature going to be the segue for your conversation, but you’re also going to assume some knowledge on their part, meaning you can dive deeper into some of the less well-known details of this feature and how this interacts with other killer features. In short, with what in effect should be a much more qualified lead, you’re going to be closer to closing the deal.
4. Planning
With sales intelligence data, you can also build a much more accurate picture not only of your sales forecasts, but also the channels that lead to sales. You will also have a clearer picture of your average customer journey length or time-to-revenue. Moreover, with the historical data from across the journey, you’ll be much better placed to allocate resources for future activities and campaigns.
Again, this means less time and resources going to waste through poor planning and forecasting. Which in turn means having the time and resources to focus on catching and pushing leads down the pipeline.
You might be interested in this post on How to predict revenue and hit your targets with Dreamdata.
The final list of Sales Intelligence Tools
In the ever-evolving landscape of sales and marketing, having access to accurate and actionable insights is crucial for driving revenue growth.
Sales Intelligence tools empower businesses with the knowledge and tools necessary to make informed decisions, identify potential customers, and optimize sales strategies.
This list showcases a range of powerful Sales Intelligence tools to help sales teams boost their effectiveness, improve lead generation, and maximize revenue generation.
Dreamdata: Dreamdata provides revenue attribution and advanced marketing analytics to help businesses understand the impact of their marketing efforts on revenue generation.
ZoomInfo: ZoomInfo offers a comprehensive database of company and contact information, enabling sales teams to find and engage with potential customers.
Clearbit: Clearbit provides enriched company and contact data, helping sales teams personalize their outreach and improve lead generation.
InsideView: InsideView offers real-time market and account intelligence, empowering sales teams to identify and target the right prospects.
Leadfeeder: Leadfeeder tracks website visitors and provides insights on companies that visit your site, helping you identify potential leads.
Owler: Owler offers competitive intelligence and company information, allowing sales teams to stay updated on industry trends and competitor activities.
InsideSales.com: InsideSales.com leverages AI and predictive analytics to provide sales teams with actionable insights and improve sales performance.
SalesIntel: SalesIntel offers a high-quality B2B contact and company database, helping sales teams identify and engage with potential customers.
HG Insights: HG Insights provides technology market intelligence, enabling sales teams to target the right accounts and drive revenue growth.
Cirrus Insight: Cirrus Insight integrates Salesforce with Gmail and Outlook, providing sales teams with email tracking, scheduling, and CRM functionalities.
Conclusion: what’s your (pipeline) race plan?
The results are in: sales intelligence data offers all the right signals to bring your efforts to full fitness.
By having a data-driven ICP and qualifications process, demand data at your fingertips, and data-led reporting for accurate planning and forecasting, you’ll be dropping those dead-end leads and accelerating your pipeline in no time.
Why not get your pipeline moving with sales intelligence data today?
