Five Steps To Develop And Improve Lead Generation And Prospect Development
By Daniel Hussem - Director of Marketing for Troparé Inc.
Click here to read the original article on Forbes.com
Digital technologies create new opportunities for business-to-business (B2B) sales and marketing. They uncover a whole new world of lead generation channels for companies and organizations of every scale.
The customer acquisition process (end to end) depends particularly on lead generation to identify and attract potential buyers and further prospect development. In my opinion, lead generation and prospect development are, in the process flow, inseparable.
As the director of marketing for a company that offers an analytics platform for both marketing data management and field sales acceleration, I have found there are five crucial steps companies must take to develop and improve lead generation and prospect development when applying data-driven technologies.
1. Build A Reliable Data Foundation
A common bottleneck for many businesses arises from complications caused by the need to combine data from various sources. Some outdated software systems simply cannot cope with the volume and diversity of disparate data. “Outdated” in this respect doesn’t just mean “obsolete.” Due to the rapid development of technologies, “outdated” means moderately lagging behind the superfast pace of innovations. The difference is enough to put the user at a competitive disadvantage. Outdated tools are inefficient, less effective and more time consuming, and they compound the opportunity for errors because they do not have the corresponding operational capacity.
Outdated sales and marketing technology includes those systems that limit your company with a legacy burden. Big data brings an opportunity to have a full-scale view of your business. However, to get big data distilled into smart data, you need to learn how to combine and efficiently analyze various types of data. Information from disparate sources isn’t easily linkable, it’s prone to dirty data and often results in unstructured or semi-structured data.
By using a data-agnostic technology platform, you can typically avoid data integration, compatibility and performance issues due to those platforms’ inherent nature to handle and process various data types and formats. This approach also helps steer clear of unforeseen operating costs and dampened scalability issues associated with being confined to only a select group of data types or formats. Additionally, keeping data safe with today’s heightened security threats requires an up-to-date and secure platform architecture.
When researching vendors to assist in establishing a reliable data foundation, there are several best practices to follow:
- Evaluate a vendor’s ability to ingest and process disparate data types.
- Include both the sales and marketing departments in the evaluation process to make sure the platform serves both their needs.
- Thoroughly review a vendor’s security infrastructure and disaster recovery plan to ensure your data is in good hands.
- Obtain upfront pricing and an actual development outline associated with any customizations you may require.
2. Translate Big Data Efficiently
The “big data bang” has created torrents of terabytes. This data covers a wide spectrum of information that can be used by companies to fine-tune their business strategies. However, for optimal information gain companies must develop “data translation” skills.
Companies that are successful in translating big data into market growth tend to follow three basic principles:
- They use analytics to identify valuable business opportunities.
- They turn these insights into actions, products or services.
- They successfully present these actions, products or services to their clients.
Companies that apply big data and analytics in their business operations demonstrate a considerable improvement in their productivity rates. Being able to translate and make sense of data transforms big data into smart, actionable data that delivers quantifiable business results.