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Lists and data go hand in hand. Some types of data can inform the way you develop your list, while other types of data are gleaned from already existing lists. According to NetProspex, more than 60% of B2B companies rely on “unreliable” data to fuel demand generation. In fact, bad data or poor data quality costs US businesses $600 billion annually. We want to show your company how you can prevent wasting money and use data to your advantage. In order to see success in inside sales, learn how to use data to drive sales by monitoring events that affect your B2B buyer and monitoring predictive analytics to inform you of the next step to take. Three different types of data can help you do this:
Big data is a collection of data sets so large and complex that it becomes difficu to proces using CRMs or traditional data processing applications. The more data, the more diffic it is to visualize, capture, store, and analyze. According to The Big Data and Analytics Hub, only 23% of organizations that were assessed have an enterprise-wide big data strategy. However, big data helps sales reps spot large business trends and prospect accordingly. Data from diffeent social media outlets, industry trends, economic trends, transactions to videos, text documents, audio file are all outlets where Big Data can be aggregated. It comes in various formats so it is very challenging to collect and make factual sense from that data. Now that we know all of this information is available and we have the capability of capturing and recording the information, it is up to executives and marketers to understand how to utilize big data for their sales prospecting efforts whether they do so in-house or choose to pay for aggregated data business intelligence. While big data might seem like a catch-all term — it certainly includes trigger data and analytic data within — understanding how to use a large amount of data to your company’s advantage is crucial. Use the data to listen to your prospects and from those insights create the optimal message and contact them at the best predicted time possible.
Triggered Event Data
Triggered event data is information about an event that elicits a specific response in the sales cycle. In marketing, triggered event data can help guide workflows in the right direction. In inside sales, trigger data can help sales reps make more precise calls to the right buyers in their time of need. Here’s the process: First a trigger event occurs, which can be defined as an event that creates a pain or challenge, which precipitates a specific need to alleviate that expressed pain or challenge. Because of this newly created need, sales and marketing professionals can create a targeted list of prospects who have been or will be affected by the trigger event and will need to seek out a fitting solution for their company. The data in this list is trigger data, or data collected from a trigger event.
Inside sales managers may want to purchase a very specific list, or the trigger event could help inside sales reps focus on a targeted area of an existing list. For example, if Company ABC has a security breach regarding their financial software, that would be considered a trigger event and would elicit a response and create a need. The response would be finding a better solution to protect their financial information, and the created need would be something such as an upgrade to their existing security software that failed, working with their current software provider to ensure this will not happen again, or totally replacing their security software and purchasing from a new provider. If a security software company is notified of this trigger event through media coverage or through a business intelligence SaaS tool, the moment they are notified of the breach they could then immediately reach out to Company ABC and offer their solution. When using sales triggers to prospect, inside sales reps know where to focus their prospecting energy immediately. However, those who use trigger data need to constantly monitor events and be quick on the uptake.
Analytic Data: Predictive Analytics and Data Driven Sales
While trigger data helps inside sales reps pinpoint exactly when and where to prospect, analytic data helps inside sales reps find target audiences that are more likely to buy so they can focus their prospecting efforts there. Why would you want to use analytic data as well as trigger data? Analytic data can predict when and what companies are more likely to buy your service or product. Depending on what company you elect to use, their service can aggregate data from social media, industry trends, ERP data, data within your CRM, and other algorithms that calculate how likely a prospect would purchase your product or service.
Many inside sales reps have analytical data bolted into their CRM. When a sales rep looks up a company, their CRM will pull that analytical data on that company such as company size, revenue, number of employees, etc. For example, an inside sales rep is working on a health insurance project and is trying to convert prospects from one broker and carrier to another. If the sales rep is using predictive analytics they would be able to note that the prospect’s renewal date is not per calendar year and that they renew every November. Their buying cycle is therefore not the typical calendar year, so the sales rep can then assume it’s best to reach out to them in September before they start looking for other options. There are many different scenarios where inside sales reps can use these insights to better allocate their time and focus on prospects that are more likely to buy rather than those who are lower on the list. Here’s another example: an inside sales rep is selling high value technology. While using their predictive analytics service, they notice that 30% of their current list doesn’t even have the funding to purchase the technology they are selling. This data allows them to easily disqualify that portion of their list and spend more time prospecting those who have the necessary funds to purchase the technology.
Data-driven sales uses research from previous sales interactions to improve and forecast future conversions. Knowing how to manage your sales data, keep the information up to date, and monitor it for changes is essential. Data-driven prospecting, using the information and expertise at hand to analytically prospect, has better accuracy and provides a stronger and more accurate forecast. Using this method to prospect, along with predictive analytics, and lastly merging that data with sales reps’ experience will significantly increase your sales reps’ conversion rates.
What types of data do you use to develop lists and analyze and predict outcomes? Don’t forget to download our eBook to learn more prospecting and management strategies!VN:R_U [1.9.7_1111]How to Use Three Types of Data to Drive B2B Sales,
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