The importance of ‘Channel Analytics’ lies in choosing the right mode of distribution. Jon Voorhees while talking about ‘Distribution Planning Analytics’ emphasized on Branch Distribution Analytics mainly focusing on the question that how many branches are needed to have an efficient distribution system in the market including distribution concepts, the impact of over-branding and the implications of having too much distribution.
The key analytic concepts include:
- Competitive Saturation is households and branch in total. The more is the competition in an area; the smaller is everyone’s fair share.
- Branch Density is the number of branches for a given area. There have to be enough branches to be seen as competitive and convenient but too many create excessive overlap and inefficiency. The aim should be to be a large player in that particular market, the more competitively saturated it is the more branches are needed.
- Fair Share means branch share is equal to the deposit share.
- S-Curve shows the reality where smaller branch share banks rarely get their fair share.
- Power Ratio is the deposit share or branch share and measures the efficiency or effectiveness of the distribution.
The Net Effect is described in the Efficiency Ratio which is how much expense to spend for every dollar spent to get it. The formula for this is-
Retail Bank Target 50% as efficient and challenge lies in finding where there is an excess. Branch Trade Area is unique that can be customized based on recent sales activity or estimated based on local daytime population density. Branch can also be based on drive times except in dense urban downtowns. All in all some overlap is required for member convenience but too much creates inefficiency.
Next comes ‘Using Mobile Location Data to Improve Marketing Effectiveness’ where Ron Shevlin highlighted four important pieces of information required: Device ID, Geographic location, Dwell time and Frequency. With the help of this information, the whereabouts at a particular time can be tracked as well as it helps in building a mobile behavioral trajectory of the members by giving the place, persistence, period and path of the members. It is important to understand certain things to improve marketing strategy and intelligence such as Customer Profile Enhancement, Customer Segmentation, Campaign Management, Competitive Intelligence, and Branch Performance. The particular challenges faced by marketers are:
- Credit Unions don’t know their members as well as they think they do. They don’t know who their members bank with, where they work, how far they commute, where do they shop and when do they shop and bank which are all aspects to study.
- Marketing Campaigns are rigid. The offers have to be tested to examine which branch benefits from it and accordingly make modifications in the campaign and ultimately track the performance.
- Financial Institutions have little Competitive Intelligence. This entails the percentage of members visiting other bank branches and percentage of customers that made two or more branch visits.
The important thing is to Move the Data Strategy from DEFENSE TO OFFENSE. The defensive purposes are regulatory reasons and compliances while offensive is marketing and offers. The success factor to mobile location data is incorporating new data sources in the marketing strategy with increased personnel to implement and integrate it.
Lastly, Adam Hass gave ‘Mobile App Analytics to Make Data-Driven Decisions’. Mobile app analytics is the practice of collecting user behavior data, determining intent from those metrics and taking action to drive the desired result. Common objectives include retention, engagement, and conversion. It helps to study the users’ interaction with your app, documenting behaviors that can help you improve their user experience and continue to engage and delight the members. Mobile analytics can help deliver better apps. According to a study, 65% members downloaded mobile banking app, and another study says that 47% prefer to use Financial Institution mobile app. The purpose of analytics is to measure results at any angle. Success can be measured against the current strategy and information can be used to tune the strategy further. Mobile Banking Analytics can measure the impact of offering promotions and products, track the offers that monetize the channel, determine the most popular feature, reduce fraud through behavioral trends and show problematic areas.
The Maturity Model of Sophistication of Mobile Banking Apps relays information to customers through enabling transactions, interacting, lifestyle management and engaging in largely non-financial activities. The important metrics or key performance indicators can be used to foretell success of the app, insight into why an app is performing in a certain way and how to improve a certain area that isn’t performing well. These metrics are a number of downloads and number of users which generally give a false picture, retention rate and active users, crash analysis and number of users during the initial stage, user journeys and conversions, service penetration, promotion or shopping, and location information. The pitfalls to avoid here are vanity metrics such as the number of downloads, no ownership scenario, and no analytics plan.
It all trickles down to member experience as the data gathered from app usage gives a detailed peek into customers. It is an opportunity to guide product development and sales and marketing efforts around their preference. Fine-tuning analytics strategy over time can lead to long-term growth, sustainability and a higher rate of interests.