Mar 25, 2013

Three Ways To Grow Your Top-Line

Nick Pohlman

Nick Pohlman

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People often think of Business Intelligence (BI) as charts and graphs that do little more than measure various historical aspects of a business (e.g., revenue, unit sales, etc.) so the decision makers can then know how the business has performed. In recent years, however, the capabilities of business intelligence have enabled companies to become much more forward-looking. Using the BI tools and methodologies now available, companies can develop strong analytics-based strategies by gleaning insights from their data, giving them a distinct competitive advantage in a rapidly changing marketplace. Find below three different ways companies can use forward-looking analytics to transform insight into effective action that brings measurable business improvement.

1) Synthesize information into intelligence

BI is rapidly becoming much more than simple reporting. When businesses contextualize multiple data points, apply market segmentation, and enhance their own analytics by pulling in data from outside sources, the conversion of information into intelligence begins, and the strategies to gain competitive advantages are hatched.

Information, after all, is merely the knowledge of data or facts. Intelligence, however, is the synthesis of information, sometimes multiple pieces of information, through an analytical lens which can be used for making business decisions.

For example, the statement “TTM (Trailing Twelve Months) Sales to Customer A are 450 units” is information. It is a statement of fact, but it does not provide enough context in order to take a business action.

Now consider the following additional pieces of information:

  • Our company’s TTM Sales to Customer A are 450 units (Information)

  • Our company’s TTM Sales to Customer A are eroding (declining) by 40% Year over Year (YOY) (Information)

  • Until now, sales to Customer A have never had a YOY decline in unit sales (Information)

  • Customer A’s own total sales increased during the TTM period (Information)

  • Customer A is in an area where there is population growth and significant new construction (Information)

  • Total market sales went up in Customer A’s territory (Information)

  • Synthesizing all the previous statements leads us to conclude that the reason for our sales decline is that competitors have started selling products to Customer A, taking some of our sales. (Intelligence!)

In this example, the knowledge that we have TTM sales to Customer A of 450 units is useful, but does not help us drive a strategic business action.  Combining several pieces of information together, often from different data sources, and analyzing what those pieces of information mean in context becomes intelligence. The intelligence, in turn, facilitates the informed development and execution of a strategic play in the market place.

In this instance, for Customer A, we have identified the profile of a customer that is eroding due to competitor encroachment. The “competitor erosion” customer profile can now be used to find instances of other competitor encroachments throughout the customer network. For companies with tens or hundreds of thousands of customer locations, a strategic market share protection play can now be developed by deploying sales resources specifically to the customers where the company notices the “competitor erosion” sales pattern. In this case, intelligence has become an effective tool to identify and respond to an attack from competitors, ultimately protecting market share.

General Motors has become one of the decade’s greatest examples of data-driven strategy. In the early 2000s, GM had too many brands and too many dealer locations. Dealerships were poorly located and competed with each other, and a profusion of brands with no clear differentiation cannibalized each other’s sales. As part of the turnaround plan during the bankruptcy, GM began eliminating unprofitable brands and simplifying their product lines. To do this, GM relied heavily on data-driven intelligence. Customer profiles were created and combined with sales data, full geographic demographic data (income, age, number of children, etc.), traffic data, and competitor data to create a business intelligence platform that powered the analytics of the dealer network transformation. Bruce Wong, Manager of Advanced Analytics for GM, coined the term “Demand Surface” to refer to this new BI platform.

Using Demand Surface, GM identified underperforming dealers that were poorly located, underperforming dealers that had to be closed to eliminate cannibalization, dealers that needed to be relocated, affirmations for the remaining active dealerships, and locations for new dealerships based on predicted future population and demographic changes. This analysis was performed geospatially using the powerful ESRI geospatial intelligence engine. Ultimately, the dealer network emerged leaner, stronger and better positioned to retake market share than at any time in the last thirty years!

2) Segment your market in order to optimize your product, pricing, and promotions

Market Segmentation data can powerfully augment your customer data. By tagging your customers with market segmentation data and integrating sales data, you can develop specific and rich customer profiles. This powerful piece of intelligence can influence product mix at each customer location in the brick and mortar world or influence cross selling and upselling in the eCommerce world. Moreover, the overlaying of product mix demand with market segmentation data provides a useful matrix to target where a company should search for new customers.

NAICS (North American Industry Classification System) Codes can be a powerful tool to help build this matrix. NAICS codes are the modern version of the SIC code and have a greater depth and breadth of classification. SIC Codes were last updated in 1987 (before the internet and cell phone technology became ubiquitous), and do not contain classifications for any industries created since then, e.g. internet service providers, cloud computing companies, web hosting companies, e-retailers, web development companies, mobile phone retailers, etc. Purchasing NAICS codes for existing customers from a data provider like Equifax is the most common way to add this attribute to a customer profile. By adding the NAICS classification dimension, optics can be developed into the codes and categories most commonly attached to the growing parts of a customer base. New potential customers can now be identified by their classifications in the NAICS system.

Once the full customer profiles for growing customers are identified, including attributes like price sensitivity, the sales and marketing teams can develop strategies to grow a company’s presence with targeted high-growth consumers. For example, a price point for a given product at a luxury or upscale retailer may be higher than the price point for that same product when sold at a discount retailer. An analysis of the price sensitivity of the customer base that shops at each retailer can determine the exact price point or price ranges. By analyzing price sensitivity in each growth segment, marketers can develop product and pricing offerings that are known to beat the competition, retain customers, and select an intelligence-driven price point aggressive enough to gain market share but not so aggressive that profitability is sacrificed for the growth.

For example, NAICS Code 23811 classifies Poured Concrete Foundation and Structure Contractors. By identifying geographies that have heavy growth in residential and commercial construction starts relative to available capacity, suppliers of materials, services and equipment can target prospects by NAICS code and propose a price point that is in alignment with the construction starts’ growth percentage and available capacity in the marketplace. Higher rates of increase in construction starts and lower available capacity will equate to a lower level of price sensitivity in the prospective customers. Thus, suppliers can feel confident pricing their products and services higher and their promotional discounts lower.

3) Manage your data according to a well-thought-out plan

The better your data, the better the results will be from your analytics. In the pursuit of optimal intelligence, it is easy to forget how important it is to manage basic data well. A complete data management strategy must be developed for the entire life cycle of the data – from creation to archival or deletion. It is not uncommon for companies to perform a solid piece of data-driven analysis that produces an excellent strategic business recommendation, and then the companies fail to act on the recommendation because they do not trust the underlying data. However, data management is not limited to data governance and data quality. Data management also means ensuring that the data is accessible to the decision makers and analysts and that it is presented in the most useful format. So, good data management must be solid part of the overall strategy to leverage data as a strategic competitive advantage.

At the most basic level, data management must include the following:

  • Data Governance – ensuring consistent capturing of data across the enterprise

  • Timely Access – data must be made available to the business decision makers quickly enough to see trends developing and mitigate negative impacts as soon as possible

  • Data Quality – monitoring and dictating how data is moved, transformed and aggregated for analysis

  • Master Data Management – storing the same data in the same consistent place. Having a single version of the truth will ensure all the organizations within the enterprise are making decisions based on the same facts (rather than on two different versions of the same number, each of which lives in two different systems)

  • Data Lifecycle – how long is data worth keeping in the analytical engine?  For a social media business, it may be hours or days, for a bring and mortar company it may be 3-5 years

Leveraging business intelligence in the development of growth strategies converts growth from pure art to something that is a much better mixture of art and science. Financial investments and deployment of valuable resources can be made with much more certainty that the returns will be worth the outlay. Companies that embrace the three steps of synthesizing information into intelligence, augmenting and segmenting the data to focus on the intelligence that is important to strategy development and managing their data well ensure that the intelligence engine is being filled with the purest fuel possible. A company running on a powerful intelligence engine is positioned to become the market leader and more importantly retain the position as the market leader in a fast changing world.

Credera has significant experience helping clients increase their revenue with intelligent data and analytical tools. If you have questions about this blog or a technology decision you are facing, please contact us at 972.759.1836. We’re here to help.

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