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Big Data

15 Ways Business Development Leaders Are Leveraging Big Data

Nastel Technologies®
December 26, 2019

Big data has revolutionized nearly every industry by giving business leaders unprecedented access to rich, granular information about its customers. This data can significantly improve your company’s business development efforts, but you have to know how to properly gather and implement it.

To help you, we asked a panel of Forbes Business Development Council members how they’re using big data for their biz dev teams. Their best responses are below. Follow their recommendations to start implementing big data in a meaningful and effective way for your business.

1. Segmentation

Big data, as its name says, is no more than that: just a lot of data. We use it to create clusters in different segmentation models where we integrate different variables. The advantage of having a lot of data is you can really go deep and segment with more variables without losing the representative scope. – David Mahbub, Field Agent

2. Analyzing Usage Patterns of Partner-Integrated Solutions

On our SaaS platform, we track integration points with third-party solutions. By loading this data into Google BigQuery, we are able to isolate partner traffic and see which ones are really driving product usage. Customers won’t always know what’s important to them, but the truth is in the logs if you know how to look! – Kit Merker, Meshmark

3. Responsive Tracking

Using gated content, segmented lists, social media insights and Google Analytics allows our digital marketing team and our business development team to work together to move clients through the sales funnel. Tracking how and why clients visit our site helps us gauge engagement, needs and interests. Combining data with intel from the BD team allows us to craft meaningful content that provides our clients with value. – Lauren Homme, E4H Environments for Health Architecture

4. Sales Rep Workload Capacity

We use big data to segment accounts by tier based on monthly recurring revenue, average deal size, total new clients per month, win rates, sales cycle time and churn. We use predictive analytics to forecast future projections. We then use the data based on the number of accounts, rep productivity and total annual selling time to model the number of sales reps required by tier along with total compensation and cost of revenue. – Jeff Harris, Nomos One

5. Predicting Upcoming Trends

We use data history to anticipate upcoming trends. We then compare our historical data to client profiles we’ve generated with AI technology to better identify who we should be targeting. We can also use this data to anticipate the decisions a prospect will make based on what other companies do and why they’re making the choices they are making. From there, we can adjust our strategy accordingly. – Christian Valiulis, Automatic Payroll Systems

6. Introducing New Products

We’re using big data to develop and introduce a new product. Research and analysis showed us that our independent associates and their customers are hungry for creating a seamless membership experience that provides value in the form of cash back on items and savings in the form of unpublished rates on travel like hotels, car rentals, cruises, condos and more. – Mark “Bouncer” Schiro, Kynect

7. Identifying The Right Sales Activities To Focus On

Good data is more important than big data. Identifying variables that can be reliably measured and have a strong correlation to desired outcomes is more important than the total number of variables measured. By focusing on good data, we target opportunities that are most likely to close, and we continue to hone in on the sales activities that are most likely to lead to closed business. – Brandon Rigoni, Lincoln Industries

8. Management And Coaching

The ability to understand how activities drive results is the No. 1 most powerful tool a sales leader has at their disposal. Use data collected to reverse engineer the optimal KPIs required to achieve milestones and targets. These same data points can also be used to drive more educated coaching conversations, optimizing a team’s performance. – Scott Douglas Clary, ROI Overload

9. Analyzing Voice Data

The most impactful way we have used big data with our business development team is by using AI to analyze voice data from sales conversations. There is so much that is said and done in conversations that impact sales outcomes. By understanding how conversations influence revenue won or lost, we have been able to significantly improve our effectiveness. – Howard Brown, RingDNA | Inside sales & enterprise sales acceleration software

10. Dashboard Reporting

Big data, to me, is dashboard reporting. We use it daily to see everything that is closed and in the pipeline. It’s important to have a handle on trends when they start and react accordingly. And that’s the reason we’ve been looking at dashboard reporting for years. We can get new ways of seeing technology, but the strategy of looking at our numbers every day is more important than any program. – Wayne Elsey, Elsey Enterprises

This article originally appeared on To read the full article and see the images, click here.

Nastel Technologies uses machine learning to detect anomalies, behavior and sentiment, accelerate decisions, satisfy customers, innovate continuously.  To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s AutoPilot® for Analytics fuses:

  • Advanced predictive anomaly detection, Bayesian Classification and other machine learning algorithms
  • Raw information handling and analytics speed
  • End-to-end business transaction tracking that spans technologies, tiers, and organizations
  • Intuitive, easy-to-use data visualizations and dashboards

Nastel Technologies is the global leader in Integration Infrastructure Management (i2M). It helps companies achieve flawless delivery of digital services powered by integration infrastructure by delivering tools for Middleware Management, Monitoring, Tracking, and Analytics to detect anomalies, accelerate decisions, and enable customers to constantly innovate, to answer business-centric questions, and provide actionable guidance for decision-makers. It is particularly focused on IBM MQ, Apache Kafka, Solace, TIBCO EMS, ACE/IIB and also supports RabbitMQ, ActiveMQ, Blockchain, IOT, DataPower, MFT, IBM Cloud Pak for Integration and many more.


The Nastel i2M Platform provides:


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