How will data intelligence transform the enterprise?

Traditional business methods aren’t enough to help businesses stay competitive in today’s data-driven world. Data science outperforms these outdated methods, especially in certain key areas

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As long as people have been doing business they’ve been looking for ways to improve their products, fine-tune their process, and reach more customers. There have been some truly innovative techniques developed over the years. However, few have been as potentially game-changing as data intelligence. Advances in data science make it easier than ever to organize a company’s data into actionable insights.

The impact is so striking that using non-data-based methods alone is no longer enough to stay competitive. There’s too much information available for people to handle in any reasonable amount of time. Data intelligence catches that information and filters out useful data for human attention. To see this effect in action, take a look at how data intelligence outperforms traditional business practices in three common areas.

Customer segmentation and profiling

Old school

Customer segmentation is traditionally done according to demographics that are assumed to have the most significant impact on purchasing habits. These generally include things like:

  • age
  • gender
  • income
  • ZIP code
  • marital status

While they are worth considering, these demographic categories are too broad. Personalization is a huge trend in marketing; designing marketing strategies based on raw demographics can alienate potential customers and lead to underperforming campaigns.

Additionally, many businesses are more familiar with “personas” than “customer profiles.” Personas are aspirational. They’re created by marketing and sales teams as a way to outline their ideal customer in each segment. The idea is that they should shape their marketing to attract that perfect customer.

Personas use information from market research, focus groups, surveys, and similar opinion-gathering methods. This data inevitably contains assumptions about what is and isn’t relevant or what certain answers imply about a customer’s intent. It’s highly subjective. Because of this, personas are more useful as an aspirational tool than as marketing guidance.

Data science

Data intelligence allows a company to combine all their data during segmentation. They use information from marketing campaigns, past sales, external data about market conditions and customers, social media, customer loyalty program, in-store interactions, and more for a fuller picture of their customers.

Techniques like machine learning remove much of the human bias from the process, too. Intelligent customer segmentation starts with no assumptions and finds shared characteristics among customers beyond simple demographics. Demographics were mainly popular in the first place for lack of a better option. Now, marketers can sort customers by factors like hobbies, mutual interests, career, family structure, and other lifestyle details.

Customer profiles built on this type of data are grounded in reality, not ambition. They describe the customers who are already using the company and identify the things that encourage conversion and raise potential lifetime value. Companies can then use this data to guide their marketing and sales strategies.

Example: During the 2012 presidential campaign, former President Barack Obama’s campaign manager, Jim Messina, used data science to find create dynamic profiles of supporters. The deeper understanding of potential donors and volunteers helped them raise a staggering $1 billion and won the candidate the election.

Marketing campaigns

Old school

Without analytics software, marketing decisions have to be based on a combination of sales projections and past seasonal sales. Some companies use weekly sales numbers and operational figures. It’s hard to process all that data in time to be immediately useful, though, so the result is typically a shallow snapshot taken out of context.

Tracking projections can help guide overall strategy, but they inherently rely on outdated information. Opportunities might not be spotted until they’ve passed. This reduces the impact of flash sales and other time-sensitive events.

Data science

Real-time data analytics is where data science really shines. These programs combine data from multiple sources and analyze it as it’s being collected to provide immediate, timely insights based on:

  • regional sales patterns
  • inventory levels
  • local events
  • past sales history
  • seasonal factors

Streaming analytics suggests actions that meet customer demand as it rises, driving revenue and improving customer satisfaction.

Example: Dickey’s BBQ Pit centralized data analysis across its stores, processing story-by-store data every 20 minutes. The restaurant chain can now adjust promotions every 12 to 24 hours as opposed to weekly.

Logistics

Old school

Logistics is a place where data has a massive impact. It’s a highly complex discipline that’s influenced by a huge variety of factors. Some are obvious (weather, vendor readiness, seasonal events) while others are less obvious. Because individual managers decide what is and isn’t important based on their subjective experience, these less obvious variables are often overlooked.

Troubleshooting logistics issues is a headache as well. Without data intelligence, managers spend hours gathering information and analyzing it manually before they can even identify the problem, let alone resolve it. That’s a waste of valuable experienced labor that could be better used elsewhere.

Data science

Good logistics planning relies on timely information, and data intelligence methods like streaming analytics provide that information. Analyzing multiple data streams creates a real-time, evolving picture of operations with insights like:

  • accurate delivery timelines
  • best dates for an event
  • external events likely to affect plans
  • potential route hazards
  • ideal locations for warehouses or resupply stops

Processing the data and presenting it in a dynamic visual format often reveals unexpected patterns. Some inefficiencies and redundancies in processes are hard to detect in raw data. For instance, information generated naturally in one department might not be passed on, forcing other departments to recreate it. There may also be a staffing imbalance relative to customer volume at certain times of day that could have been avoided with advance warning. Whatever form these logistical issues take, data intelligence helps find a solution.

Example: For UPS, small changes in routes have huge results: saving one mile a day per driver saves the company as much as $50 million dollars every year. Since implementing the Orion route optimization system, UPS has trimmed more than 364 million miles from routes globally.

Controlling the hype

Data intelligence shouldn’t be seen as a panacea for all enterprise woes. Unrealistic expectations can kill a data science project as easily as a lack of support. Companies get caught up in the hype surrounding a new tool and expect immediate ROI. When the expected results don’t materialize, the company becomes disillusioned and labels data science a failure. This puts them at a disadvantage against their better-informed competitors.

Stay out of the hype cycle by viewing data intelligence as decision support, not a decision maker. Analytics aren’t magic; they simply provide targeted insights and suggestions that help executives shape corporate strategy. Maintaining realistic expectations about their potential is a step towards realizing lasting results from data intelligence programs.

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