Bridging Technology Investments’ Value Creation Gap

Despite investing millions into digital, data, and analytics transformation initiatives, several enterprises find themselves grappling with a frustrating reality: the value they expected remains elusive.

Why do so many organizations struggle to realize the promised benefits of their technology investments? What strategies can leaders employ to close this value creation gap?

At Kaizen, our entire approach revolves around a value-first approach to technology transformation projects.

For one of North America’s leading Automotive OEMs, Team Kaizen was brought in to build a Data Lake Solution to track used car sales data. We turned the engagement on its head and built a Certified Pre-Owned Vehicle Revenue Optimization Platform that the OEM now uses as a central dashboard to make used car sales-related decisions. Within a year of implementation, the company was able increase certified used car sales by 5% and profits by 3%. The vital element that made the difference was how we started looking at the opportunity: Instead of just looking at it as a data lake implementation project, our focus was on delivering sales and profit growth. The entire solution was architected with this mindset. And, it was possible because the engagement team came with deep domain expertise, in addition to cutting-edge technical capabilities.

In this blog, we decode for our readers The Kaizen Way of driving technology transformation engagements, where value creation lies at the heart of everything we do.

Where Technology Transformation Projects Go Wrong

In several technology initiatives, five typical mistakes undermine teams right from the start.

Lack of Clear Strategic Alignment: Often, technology investments are made without a clear alignment with the organization’s overall strategic objectives. This lack of alignment leads to disjointed efforts and hampers the realization of tangible business outcomes.

Inadequate Data Governance: Data is the lifeblood of digital transformation initiatives. However, many organizations struggle with data governance issues such as poor data quality, incomplete data sets, and lack of data management processes, which hinder the effectiveness of analytics efforts.

Insufficient Change Management: Successful technology transformations require more than just the implementation of new technologies, tools, and systems. They necessitate a cultural shift within the organization, along with robust change management practices to ensure adoption and sustainability of the new way of operating.

Overemphasis on Technology: While technology plays a crucial role in digital/data transformation initiatives, focusing solely on technology without addressing the underlying business processes and organizational capabilities can lead to suboptimal outcomes.

Short-term Mindset: Transformative initiatives often require a long-term perspective, yet many organizations prioritize short-term gains over sustainable, long-term value creation. This shortsightedness limits the potential impact of technology investments.

The Kaizen Way: Focusing on the Business Impact of Technology Investments

Our guiding principle at Kaizen is to deliver “Maximum Business Value with Optimal Use of Technology”. We believe transformation projects can be categorized into one of these four quadrants (showcased below).

The Road to Value Realization

For a Fortune 100 Food Distribution Company, Kaizen was roped in to build a Price Analytics Engine. The company was grappling with a few key challenges:

• Prices varied for same item across customers
• Cost+ approach to pricing was resulting in missed opportunities
• Frequent cost changes were resulting in “out-of-market” prices

The solution we built was a Contextual Pricing Framework Model, which factored in key purchase drivers, market factors like inflation and deflation and customer willingness to pay. But it’s important to understand the process we followed that led us to this solution:

1. Assembling analysis data sets: Our first step was to assemble data from multiple data sources, and then we developed a representative dataset for statistical modeling within one week.
2. Rapidly prototype predictive models: Next, we developed predictive models in short bursts of 4-6 weeks. We were able to build models to analytically suggest pricing for existing items with increased sensitivity to inflation and deflation.
3. Contextual Analytics: Leverage historical sales as a proxy and several other data points to recommend analytically driven pricing for new items.
4. Dynamic Pricing Model: We built a dynamic pricing model that enabled the following:
   a. Drive strategic price changes with real-time data and analysis
   b. Balance price increases vs. volume loss
   c. Lower risk and improve margins
   d. Our model was designed to deliver an optimal margin for each customer across their basket of items
5. Continuous Improvement: Work closely with client partners to continually improve models.

Our approach ensured that the client was able to witness quick wins and proof of concepts right from the get-go, which gave them the confidence that we were moving towards a clear business and strategic goal. Within 1-year, we enabled the following big wins:

• $70M Revenue Uplift, thanks to better pricing strategies, with the support of contextual price modeling
• 1.5% Margin Uplift, thanks to sophisticated modeling capabilities that took into account inflation, deflation, input costs, etc.

From our learnings over the years, we’ve put together a few questions to ask before embarking on a technology transformation project:

1. What are the key conditions that need to be in place before initiating a transformation project?
2. How do you determine when your organization is truly ready to embark on a transformation journey?
3. What are the primary business challenges that are being addressed?
4. What should be the ideal timeframe for the transformation project, and how do you ensure realistic expectations?
5. Who owns the transformation initiative? And, which teams do we need the support of?
6. How do you ensure alignment and buy-in from all stakeholders throughout the transformation process?
7. What strategies can be employed to effectively manage resistance to change within the organization?
8. Are there specific metrics or KPIs that should be established to track the progress and success of the transformation?
9. How do you adapt and tailor your transformation approach to suit the unique needs and challenges of your organization? In the process, how to we adapt manuals, FAQs, and general searches for work items/processes to AI based tools (such as Live Virtual Assistants) that can help users get to the answers in a quicker, more user-friendly way?
10. Any transformation is an ongoing process of continuous improvement, so how can we ensure that? How do we “change” the company’s operating rhythm to adapt to this transformed approach? Based on experience and best practices, how can we autonomously finetune the AI engine to learn from the new process being executed, help identify improvements, and perform the majority of the improved process tasks?

In conclusion, bridging the value creation gap requires a concerted effort to align technology investments with strategic objectives, foster organizational readiness for change, break down data silos, take a holistic approach to transformation, invest in talent development, and ask the right questions before embarking on a transformation journey.

By adopting a disciplined approach inspired by the principles of continuous improvement and quick wins, organizations can unlock the full potential of their technology investments and drive strategic growth.

Give us a shout if you’d like to embark on a technology transformation journey with a clear focus on business impact.
https://www.kaizenanalytix.com/contact