Can giving your customers accurate proximity data to your brick and mortar store help your business? There are many Store Locator solutions on the market which businesses use to help potential customers come to the decision that the proximity will result in an actual visit. This usually requires integrating a third party web site widget or licensing an API. When integrated, most offer a sorted list of ascending distances from the starting location to the respective stores. Distances returned are usually a computation using the ‘great-circle distance formula’, or simply put an ‘As the Crow Flies’ distance. This means that the returned distance is the shortest distance between two points on the earth’s surface – unimpeded by any physical barrier.
This great-circle distance is commonly used when the calling application prompts the customer to specify a maximum distance from a choice of radius in miles (or kilometers). Calculations are quick, and the shortest distances can be visually appealing to the user when multiple results are mapped and the nearest location makes logical sense compared to ones further away.
Then, by selecting the store location choice of nearest distance, the user can get directions, which usually uses a mapping API to get an actual driving distance. At this point, the customer is facing an accurate representation of what it’s going to take to get to that destination.
But, depending on the nature of the area, this mapped distance can be quite different from the first visual location. What about the implementation where the route is more rural, contains mountains or river crossings for example? The greater the distance from start location to the desired destination, the less accurate the computed distance will be. Does the user decide the store isn’t worth the trip, and choose another consumer website with the hope of a better travel trip? Given that the customer has decided against visiting ‘nearest’ answer, it’s unlikely that this customer will start investigating the secondary great circle route destinations on the returned list.
In these cases, it might be useful to have a solution which computes route (road) distances and a general sense of travel time during the first calculation. At first glance, if using a common dealer locator, the nearest store may be a distance of 15 miles for example. The next closet might be 25 miles and the customer may never consider making that the destination of choice. The customer will click and get directions for the closer location. But what if the actual driving distance for that chosen location is actually 24 miles (due to indirect secondary roads), and the second nearest location’s distance is still only 25 miles (due to a more direct route). The consumer site could be possibly steering the potential customer from a more desirable destination, and in some cases, a shorter or quicker driving route.
Having a solution where the customer is presented with the truest picture by acting upon a single web control as opposed to requiring subsequent trial and error actions – ie. getting the directions for alternate locations, could lead to better customer decisions.
Melissa’s Street Route Web Service does just that – it will return the actual street route distance in miles or kilometers and general route travel time between a starting point and destination. To learn more, visit https://i.melissa.com/giblog-geocoder-0219 or call 1-800-MELISSA.