We frequently deal with clients on the upper end of the mid-market space (~ $ $250MM-$1B in revenues) who have been successfully growing inorganically but have since stagnated. We are surprised that some of these companies still manage their business on random Excel sheets, with unified sales and profitability tracking across their global subsidiaries only possible once a week (and sometimes once a month). And even that takes hundreds of combined human hours to produce each month. There has to be a better way.

It's easy to fall victim to the many pitfalls associated with acquisitions. Companies that do not integrate seamlessly can experience profit losses after the transition phase, especially as the acquisition engine stops. However, a solid commercial analytics capability combats this issue, providing tools that help companies transition faster and realize higher profit margins. 

Challenges Acquisition-Heavy Companies Face

Companies that follow an inorganic growth strategy face many difficulties unifying their processes. This leads to data collection problems and poor data quality, hindering insights-driven selling, timely financial analysis and reporting, and profitability. This results from two key issues: system variety and a need for a unified data source that enables domain-specific, automated insights. We see this constantly, primarily in manufacturers and retailers that have grown heavily through acquisitions. 

Variety of Systems

With the pace of technological advancements along every step of a business's supply chain, it is increasingly likely that acquired companies will use systems and ERPs unfamiliar to the acquiring company. 

As a result, companies face higher difficulty in collecting data for common purposes, including:

• Managing the supply chain

• Monitoring inventory levels

• Monitoring transportation/logistics costs

• Streamlining the production process

• Managing profitability

• Delivering unified customer insights to sales teams

This challenge leads to bottlenecks like inadequately adjusted inventory levels, which cause either overstocking or understocking, hindering profitability and eroding customer satisfaction.

The impact of system diversity on operational efficiency is profound. In the past year alone, the average time required to resolve data-related issues surged by 166%, reflecting a near doubling of downtime from the previous year. This increase is especially problematic for companies expanding through acquisitions, as they must manage many different systems, ERPs, and databases. The result is a slowdown in resolving operational issues and a magnified effect on profitability due to these delays. Companies that grow through acquisitions face unique challenges, but the rewards of a higher growth rate can be significant if they can efficiently overcome these hurdles.

No Unified Data Source

Following an acquisition, it can be challenging to integrate the new data sources introduced to the company cohesively. Companies may track different metrics or follow different reporting standards, worsening the overall data quality they receive. It's not unusual to see $250MM-$1B companies whose global subsidiaries are on ten different systems/ERPs (e.g., US is on SAP, LATAM is on Netsuite, Europe on MS Dynamics) who are still managing their business on random Excel sheets and manual processes. Basic monthly reporting takes hundreds of hours, and quarterly reporting to the financial community almost always requires extension requests. 

This dramatically hinders organic growth and earnings potential.

Moreover, this lack of data unification hinders employees' productivity. 46% of data and analytics survey respondents indicated they didn't know or were uncertain about where to find information at work. Without the information they need, many teams get impacted: 

• Sales and marketing teams cannot set realistic, achievable, growth-minded targets.

• Commercial teams can't transform into insights-based sellers

• Sales teams can't be proactive about key customer actions (like churn prevention, upsell, or cross-selling)

• Leaders lack concrete data on their teams' efficiency and areas for improvement.

• Customer success or marketing teams cannot pinpoint major pain points in the customer journey.

• Marketing teams can't quantify the investment of their marketing dollars or optimize their marketing spend.

How Advanced Analytics Enhance Profit Margins

Advanced Revenue Growth Analytics tools minimize the need for manual work when collecting, unifying, and analyzing data. Popular tools in most companies' tech stacks can quickly ingest data from various ERPs in real-time or as part of a batch process, standardize and integrate into a cloud Data Warehouse like Azure, and enable user-friendly, actionable insights for commercial teams.

Of course, these tools provide the most value in the actionable, automated insights they enable for commercial teams in Finance, Sales, Supply Chain, and Marketing. 

Benefits for Pricing & Revenue Management

Advanced Revenue Growth Analytics tools collect large amounts of real-time data, help interpret it, and build models and pricing strategies to forecast and increase profit margins.

This ability simplifies the job of Pricing, Revenue Management, or Finance teams, who can focus more on asking the right questions and formulating Pricing Strategies based on insights. Once teams have adjusted for uncertainty and bias, the analytics tools can generate forecasts to inform their decisions (e.g., what should my optimal promotional investment be to maximize Profit Dollars?).

These tools also help teams better understand the implications of various pricing or promotional scenarios, alternatives they face, and any goals or constraints. Evaluating trade-offs and risks associated with each option is also more straightforward, with side-by-side comparisons generated using historical data.

Effective Business Insights

Strategic data initiatives like Commercial Analytics Transformations for firms on the bottom of the data maturity curve provide:

• Insights that yield better strategic decision-making (69%).

• Improved control of operational processes (54%).

• A greater understanding of customers (52%).

There are many reasons for these benefits:

• Unified data: Parsing and unifying data from various sources collected differently would generally take a lot of time. Automated data pipelines can do this work in seconds or minutes. 

• Real-time (or near real-time) data: Rather than waiting days or weeks until reports are manually compiled from dozens of subsidiaries, companies can access data and insights immediately, letting them track metrics with much more granularity.

• Actionable Analytics: These methods clarify complex data through intuitive visuals like charts, graphs, heatmaps, and tabular reports, including individual customer and product-level suggestions. Descriptive analytics summarizes data to show what has happened, providing snapshots of key metrics like sales, profitability, and customer behavior. Diagnostic analytics goes deeper to explain why these events occurred, using simple techniques like correlation or regression analysis to reveal patterns and causes. This streamlined approach illustrates the 'what' and the 'why' behind the data and empowers decision-makers to act effectively.

• Predictive analytics: While it is tough for a person to spot trends in a sea of numbers, algorithms (many of which are open-sourced nowadays) can identify potential causes and anticipate these trends in the future, removing much guesswork in sales forecasting, demand planning, pricing, and customer management.

Updating Traditional Tools

Manual data processes and traditional tools like Excel spreadsheets, which have long served companies well, cannot compete with the rise of BI tools and sophisticated cloud-based data warehouses. While Excel still rules the world of reporting and analytics (as it should), it is highly inefficient as a one-stop shop for data warehousing, central planning, and forecasting, especially if the underlying data prep efforts are highly manual. 

Companies that have grown through acquisitions, neglected data integration, and unified analytics as a core strategy can achieve substantial organic benefits by updating their data strategy and capability.

Scaleable, Profitable Growth

Many BI tools are "no-code" or "low-code," providing user-friendly interfaces and pre-built templates so users with minimal technical expertise can leverage their features. These features reduce the time and resources necessary for generating reports and analyzing data.

Further, the real-time data BI tools collect empower users to implement real-time alerts that identify emerging trends or potential complications. These insights help companies scale profitably in several ways:

• Optimizing supply chain processes

• Tailoring marketing campaigns

• Identifying product white space opportunities

• Optimizing prices relative to competition

• Surgically deploy promotions

• Enable proactive customer management

However, collecting this much data creates a need for massive storage space. Typically, companies store these large quantities of data in a data warehouse. Maintaining one on-premises requires a dedicated server room full of expensive hardware and experienced employees for oversight, manual upgrades, and troubleshooting.

Cloud-based data warehouses have emerged to solve this challenge. Most mid-market companies ($10MM - $1B) can significantly reduce operational costs because they require no physical hardware or allocated office space.

Moreover, scaling up a traditional data warehouse during an acquisition is costly due to the magnitude of new data. However, cloud-based solutions offer immediate and nearly unlimited storage, so companies can scale as their storage needs grow for a fraction of the cost.

Cloud-based data warehousing also enables faster implementation of new solutions. They eliminate the need to purchase and install hardware or software, which can take weeks or months.

Instead, cloud-based solutions can be up and running in days, sometimes hours, depending on the complexity of the project and the solution. As a result, businesses that need to implement new data warehousing solutions quickly after an acquisition can keep up with changing internal needs and market conditions.

Such tools enable much smoother transitions for inorganic growth than in the past, empowering companies to scale faster through acquisitions and make data and insights-driven decisions that streamline all business functions.

More Actionable Insights

Insights from advanced analytics tools provide faster, more actionable insights to the commercial teams in Finance, Sales, or Marketing. This information can increase Operating Profit in a variety of ways:

• Streamlining operations: Data on production processes, supply chain management, and other operational areas can show businesses ways to improve efficiency and reduce operating costs.

• Identifying new revenue streams: Advanced analytics tools can analyze data on customer behavior and market conditions, discovering new opportunities for businesses to expand their product or service offerings and increase their revenues.

• Improving customer retention: Customer behavior data, such as purchasing patterns from transactional data and unstructured data like reviews or customer service complaints, is challenging to sift through manually. Popular analytics methods can identify and address them to improve customer retention and increase revenue.

• Optimizing pricing strategies: Various regression and machine learning methods can determine the optimal price points for products or services by analyzing customer behavior, competitors, and market conditions.

• Identifying patterns and trends: By analyzing data on sales, customer behavior, and other vital metrics, businesses can identify patterns and trends to inform product development and marketing strategies that impact their profitability.

Upgrading to Advanced Analytics

Acquisitions can offer a rapid path to growth in many industries, but only if managed with the right tools and strategies. The right Commercial Analytics approach not only streamlines the integration of diverse systems and data sources but also unlocks significant value from these complex transitions. 

For leaders in finance, sales, marketing, and pricing, the message is clear: leveraging Revenue Growth Analytics is no longer just an option; it's necessary for competitive advantage and sustainable growth. Companies that embrace these technologies will find themselves better equipped to manage the challenges of acquisitions, more capable of turning potential disruptions into opportunities for profit and growth, and more proactive with their customer growth efforts.

Revology Analytics takes a comprehensive approach to Revenue Growth Analytics, strategically applying advanced analytics and machine learning/AI with expert Pricing Strategy to identify and leverage opportunities for companies to enhance revenue and profit. 

Get in touch to learn how Revology can address specific Revenue Growth Analytics pain points to drive profitable revenue growth.