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Implementing Data Analytics for Effective Sales Strategies in Electronics

The power of analytics is a well-established doctrine. We have witnessed and contributed to several examples of businesses that have undergone significant transformations thanks to the adoption of analytics in their business processes.

The use of analytics in business processes promises to bring the following four benefits: 

  1. Lead generation
  2. Matching people to deals
  3. Maximizing deal lifetime value
  4. Pricing intelligence

Those are all things that almost any business is highly interested in doing better. Despite the desire for these obvious benefits, many firms still believe they could do more to intentionally use analytics in their business processes. One Gartner survey 2 showed that 56% of marketing leaders are loath to increase the size of their analytics teams. The reason is that this is all easier said than done. To attain these benefits, it is often necessary to break down the silos that keep the data in multiple business systems isolated. The disconnect is not usually intentional but rather technical. There are services that will connect to business systems like ERPs, CRMs, accounting, and CMS to draw next-level insights, but that could involve adding multiple organizations to your already busy tech stack. The alternative is to find ways to get the data flowing from one system to another and enable your already capable BI team to do what they do best. That’s where Orbweaver comes in; our platform enables frictionless data exchange for the electronics industry.


The 4 Pillars of Sales and Marketing Analytics

Deriving business value from data is a process that involves unlocking data from business systems and linking those data streams together to ultimately generate actionable insights. Sales and marketing analytics have four main goals. 

Improving Lead Generation

Lead activity occurs in numerous places. The CMS is often the first point of contact. The CRM contains the business details and value of deals. The objective is to monitor and measure the progress of business deals by utilizing CRM to influence marketing activities that are often executed in the CMS. 

Matching People to Deals

The idea of improving the match of people to deals is similar to improving lead generation. Analytics can inform sales operations teams how best to allocate sales resources with account strategies. An account’s behavior can be quickly identified and acted upon using CMS, CRM, and historical sales data. For example, it takes a different strategy and perhaps different sales resources to retain accounts that have been identified with potential churn behavior than accounts poised for growth. 

Maximizing Lifetime Value

The idea of cross-sells and up-sells is highly desirable by nearly all businesses. In the electronics industry, this effort requires merging sales history with part information and even CMS data. With this, it is possible to study buying patterns and, among other things, build machine-learning models to make product recommendations based on past buying habits. The result is increased customer loyalty and reduced churn.

Pricing Intelligence

Price optimization is perhaps one of the most ambitious goals of enhancing sales strategies with analytics. Historical sales data is a primary data stream that feeds into pricing intelligence engines. The pricing engine extracts historical pricing data from files, which is then combined with other data streams to determine the final price. 

It’s Easier Said Than Done

The goals are clear; what is not is the “how.” Many of these efforts fall short because of the disparity and isolation in the data sources and stores for the systems involved. The formats can be flat files like CSVs and XLS or structured files like XML or JSON. Likewise, transmission methods can be via file systems, FTP, API, and ETL. Data analysis teams are often left with the task of resolving multiple hurdles to ingest, organize, and normalize data flows. One recent survey by Monte Carlo 1 showed that analytics teams spend as much as 60% of their time dealing with data quality and compatibility issues rather than their core goal of creating business value. Such mechanical and tactical hurdles are one of the main reasons that firms tend to fall short of the full potential of data analytics.

A Middleware Connection Fabric

One solution to the multiple problems data silos create is to use industry-specific middleware to bridge the gap between disconnected systems. The entire buying journey can be represented using a digital twin of the actions and objects involved in the process. The digital fabric of the Orbweaver middleware can break down silos by normalizing the language involved in the procurement process and thereby allow easier connections in the activities and processes of different systems.

Analytics efforts that fall short of the promise are generally not due to a lack of desire or understanding of what it can deliver. The failure of adoption and faith erosion is often due to the non-trivial mechanics of sharing data between elements of the tech stack. The is a significant distinction between data needs that are a consequence of business and data needs that are consequential to the business. Your highly talented analytics and data science teams can handle the latter, while the Orbweaver platform can handle the former.

Here, you can read about one such case where Orbweaver helped connect disparate systems to deliver increased value for Venkel, an electronics distributor.


References:

  1. https://resources.montecarlodata.com/ebooks/2022-data-quality-en
  2. https://www.gartner.com/en/marketing/insights/articles/gartner-marketing-d-a-survey-2020-analytics-teams-must-upskill
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