Following decades of data-driven digital transformation initiatives for multinational corporations, the time has arrived for midsize companies to follow suit. This moment has been hastened, in part, by the proliferation of affordable technologies that offer opportunities for organizations with limited resources to adopt advanced analytics, and midsize companies are already beginning to take advantage of this expanding technological access. Moreover, embracing data analytics is now a necessity for midsize companies that want to remain competitive, and data science training across their teams will be a critical step toward reaching this goal.
This article is the first part of a two-part series exploring data analytics applications that can propel midsize businesses to the next level across business segments. Part I will provide a glimpse into how midsize companies can follow large enterprises’ lead by tapping into their data resources to acquire and retain customers.
At the Analytics Starting Gate
While large corporations have invested in data analytics capabilities for years, most midsize companies have remained on the sidelines, observing—but not availing themselves of—the data revolution surrounding them. Spiceworks’ 2019 State of IT report found that large enterprises’ adoption rate for emerging technologies is ten times higher than the adoption rate for smaller businesses.
However—between the opportunities that come with more accessible platforms and the mounting disadvantages of failing to keep pace with today’s data-driven business practices—midsize companies are increasingly drawn toward advanced analytics, and many of these smaller ventures already incorporate data science tools and techniques into their business functions. For example, 80 percent of midsize companies plan to increase their annual AI investment, compared to 57 percent of large companies, according to a 2021 Deloitte survey. In addition, with the ascension of the citizen data scientist, midsize companies can effectively apply data science tools and techniques throughout the workforce.
Midsize organizations stand to benefit from data analytics that helps them:
- Leverage unstructured data. Unstructured data has rapidly eclipsed structured data as the most abundant source of business insights and is an increasingly important driver of business growth. However, among organizations responding to a 2019 Deloitte survey, only 18 percent reported using unstructured data in business analytics.
- Prepare the road to AI usage. A McKinsey study found that companies with solid data analytics foundations are more likely to have implemented AI tools.
- Increase automation. An especially significant contribution citizens data scientists can make across business functions is applying their data science skills toward the automation of time-consuming manual tasks.
How the Big Players Use Data Analytics
For decades, multinational corporations have analyzed internal data to increase their productivity, efficiency, and customer bases. In a 2019 Deloitte survey, respondents from large companies ranked business analytics nearly as high as risk management and reputation among organizational priorities. Further, while many midsize companies continue to rely heavily on Excel to perform business intelligence analyses, 67 percent of the large companies included in Deloitte’s study “use at least one advanced tool such as SAS, an open source tool such as R, a programming language such as Python, or an AI tool” in addition to traditional spreadsheet and business intelligence tools.
Some of the most impressive gains made as a result of these analytics have been in sales and marketing, including:
- Customer retention.
- Precision marketing.
- Customer acquisition.
- Lead generation.
- Preventing customer churn.
Multinational corporations have long used data analytics to monitor market trends and their marketing campaigns’ performance. Some effective marketing and sales analytics these leaders have pioneered include:
- Implementing efficient recommender tools..
- Tracking marketing inclusivity.
- Analyzing customer behavior to anticipate customer needs.
- Mining unstructured data to identify promising leads.
- Evaluating customer experience.
- Performing sentiment analysis.
How Midsize Companies Can Perform Sales and Marketing Analytics
Even in the absence of the expensive technologies and massive datasets available to multinational corporations, midsize companies benefit from analytics on CRM data to discover leads and identify potential conversions. For example, a marketing team at a midsize company leverages accessible analytics tools to analyze website impact and the performance of marketing campaigns. For each inbound campaign the company launches, the marketing team employs a CRM email platform to track click-thru and engagement rates, generating intelligence that informs marketing strategies. Using Google Search Console, the team also collects website data to understand how the website performs based on keywords and SEO metrics.
Similarly, the sales team applies analytics to this data to gain insights into the relationship between managed campaigns and lead generation. In addition, the sales team uses a sales CRM platform to track sales conversions and perform analytics that can drive improved decision-making about how and when to engage potential leads, how to tailor messaging, and where to focus sales resources. Sales and marketing data points can also help teams track significant metrics, such as CAC, LTV, conversion rates, and lead drop-off rates.
Preparing the Path for Data-Driven Sales and Marketing
The power of data analytics to help increase customer acquisition and retention is no longer exclusively available to industry behemoths. However, smaller organizations must build the requisite capabilities to join their progressive peers and multinational leaders in the data revolution and lay the foundation for emerging technologies, including AI.
For midsize companies, this journey begins by cultivating workforce capabilities that empower citizen data scientists across roles and departments. A global survey of 214 data and analytics leaders found that lack of proper training topped respondents’ list of barriers to BI/analytics adoption. To address this skills gap, organizations must implement affordable and adaptable data science training programs that cultivate the specialized skills and organization-wide data literacy they will need to thrive. For example, a data science training platform offering end-to-end support and comprehensive learning pathways can provide the ideal solution for midsize companies seeking accessible and customizable upskilling resources.
Please stay tuned for Part II of this series, which will touch upon advanced analytics applications that enhance HR and operations in midsize companies. In addition, Part II will explore how midsize companies can implement workforce data science training to help prepare their teams for the data science challenges of the future and support data transformation in all areas.
