A recent study found that a majority of C-level executives believe that location intelligence will be important to the overall success of their business, especially in the long term.¹

• 66 percent of executives said that it was an important part of business success today
• 78 percent said that it would be important in a year
• 85 percent said that it would be important to success in the next three years.

For a relatively new type of data, that paints a powerful picture. Not only has location data become widely used in enterprises in a short period of time, it’s continuing to gain traction.

Unfortunately, those statistics don’t paint the full picture. Location intelligence is incredibly useful – but only if it’s accurate. That same study found that ensuring data quality and accuracy is the most common challenge for enterprises using location intelligence.

Clean data is key to every area of business intelligence, but it’s a particularly large focus when it comes to location intelligence because raw location data is often dirty. Location data, whether it’s collected directly by businesses or by location technology companies, typically includes misinformation or lacks the context businesses need to use it successfully.

Misinformation: Includes inaccurate location data points from signals that are not correctly triangulated from cell towers, hypervelocity signals that show devices moving thousands of miles in an hour, and jitter in location signals due to reflections.

Lack of Context: Includes accurate location data points, but without place or event context explaining why consumers were visiting each location. For example, location data could show an audience visiting a stadium, but without knowing what event is happening at the stadium, businesses cannot pull accurate consumer insight from that data.

Problems with location data accuracy can cause location intelligence, or the insights gained from location data, to be inaccurate. That, in turn, can significantly decrease the effectiveness of any campaigns using location intelligence. For instance, advertising campaigns based on inaccurate location intelligence often see lower click-through rates and ROI. Real estate selection based on inaccurate location intelligence can result in reduced store visits and sales.

For location intelligence to be useful to businesses, it needs to be accurate. There are three key steps for ensuring your location intelligence is accurate. Whether your company is collecting location data from its own systems or purchasing third-party location information, make sure these three steps are followed.

To reduce misinformation, raw location data must be filtered and cleansed to eliminate duplicate, inaccurate, and fraudulent signals. The highest quality cleansed location data:

• Eliminates spurious signals that aren’t accurately triangulated from cell towers or reported by apps
• Uses multiple data points and calculates dwell time to ensure consumers are actually visiting a given location
• Removes hypervelocity signals and other data that can’t be verified by comparing the distance between signals received over time or other factors

Once data has been cleansed, it needs to be contextualized. Contextualization, also known as data enrichment, improves, refines, and enhances location data for more meaningful insights.

The most useful way to contextualize location data is by adding place and event information. Place and event details help companies understand what is at the location and happening at while the customer is there and provides a deeper understanding of the customer’s interests and motivations.

Consider this scenario: raw location data tells you that one of your customers was at latitude 38.958402 and longitude -77.357974. That data, contextualized with place information, can tell you that the person went to a book store and from that, you can infer that the customer is interested in books. But if the data is also contextualized with event information, you know that the bookstore was hosting a wine tasting event at the time the customer was there. That changes the story: your customer is likely more interested in wine than they are books. If the bookstore is hosting a rock-climbing event, the picture that analysis of data gives of that customer would be quite different.

After data is cleansed and enriched it needs to be analyzed based on business needs. Analysis will be different if your company is using location data for advertising or real estate selection, for example. Advertising applications will focus on the audience and their interests, using insights to better target ads, optimize ad copy, and identify lookalike audiences. Customer experience initiatives will focus on existing customers, using analytics to understand where they go before and after a visit, and how frequently and far they travel. Real estate selection analysis often focuses more heavily on foot traffic data to proposed sites.

What is your company’s goal? What information does it want from location data? Analyze the data you have based on how your business hopes to use the information it gains.

Sophisticated data cleansing techniques often result in a significant amount of data signals being eliminated. That puts businesses collecting their own data at a disadvantage – not only do they typically have access to less data, but their cleansing techniques usually aren’t as rigorous as those of dedicated location analytics companies.

If your company is collecting and analyzing its own location data (or even using location data from a third party) without applying the three steps above to a large enough data set, your location intelligence likely isn’t accurate. And that will influence the results of any campaigns or business initiatives using location intelligence.

In most cases, the best option for obtaining the most accurate location intelligence is to partner with a data provider that focuses solely on location intelligence. Location technology companies not only collect more data, but they take on the burden of both cleansing and enriching location data to provide the most complete, accurate picture to their enterprise customers. From that stage, businesses can take the lead on analyzing the data based on their needs.

http://go.carto.com/hubfs/The%20State%20of%20Location%20Intelligence%202018%20v10042017.pdf?hsCtaTracking=cb25d151-ee5f-4593-9f6a-5388d9749ebc%7C7320ab2b-7f46-481a-9b8e-a47cc20f70a1