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According to the book, ‘Marketing Metrics’, it is easier to sell a product to an existing customer (a 60-70% conversion rate) than to sell a product to a new qualified prospect (a 5-20% conversion rate). With existing clients, businesses already know their clients’ pain points, and the clients may have already become loyal to the financial institution. In the banking industry, banks often have a variety of products, but a good fraction of current customers might only utilize one or two products.

It can be challenging for a banker to sell a full range of financial products to a single customer, so front-line employees may only master a few of the high-performing financial products. Fortunately, banks have a valuable asset: customer data. With the right approach, a financial institution can evaluate their data and generate insights on cross-selling opportunities. This strategic approach to cross-selling is where predictive analytics comes in.

Here is how predictive analytics can be used for cross selling in banking:

The Power Of Predictive Analytics

The commoditization of financial products makes cross selling in banking arduous. Since customers may feel that they can get a better deal somewhere else, they might pick the most affordable product from your financial institution and hunt for other products elsewhere. This commoditization has resulted in banks bundling multiple banking products in an effort to create higher perceived value for the customer.

However, push-based selling and “one-size-fits-all” campaigns might not suffice to lure the modern-day customer. They need access to valuable products, and they need it now. Any product you bundle with the rest of your financial offerings should add the most value to their lives.

Given that banks collect data through CRM software and online tools, they can use this data to identify what their customers need. The data provides insights into:

  1. What customer to contact first

  2. What to sell them

  3. How to communicate with them

Predictive analysis allows banks to evaluate buyer behavior through recent account activities and sometimes even online activities such as reviews and complaints. Instead of offering a single generalized offer, financial institutions can personalize their products to a specific prospect group which can improve a campaign’s return on investment.

Predictive Analytics Steps

1. Start With A Question

Banks collect vast chunks of data, and they will be nothing more than data without analyzing them. To successfully identify opportunities for cross selling in banking, they must create a question and look for its answers through predictive analysis. Unlike conventional business intelligence (BI) tools that tend to be retrospective in nature, predictive analytics tools should provide insights into the future. You can get answers to questions like:

  • What customer demographics are the most likely to churn?

  • What is the estimated number of leads the institution will get from a marketing campaign?

  • What are the odds of a customer purchasing product y after purchasing product X?

  • How profitable might a specific product package be over the next two years?

2. Collect Data

The next step is to identify and collect the data that might bring the bank close enough to the answers. However, the level of confidence a bank can have in its predictive analytics software will significantly depend on the quality of the data it collects. As long as the data meets a quality threshold, it will provide trustable insights.

For the financial institutions storing outdated, inconsistent, or even incomplete client data in their CRM, data collection can become quite time-consuming. As a result, the onus is upon bank managers to spearhead data quality management that lays the foundation for a streamlined process.

Data stored within banking CRM might not be sufficient for some predictive models. Banks might need to get data from other sources. Some of these sources include:

  • ACH transactions

  • Bill payment behavior

  • Geolocation

  • Personal financial management

  • Wire & check payment data

  • Credit cards and debit cards

3. Build A Predictive Model

Next, data analysts have to create a predictive model that will define and determine the probability of specific events happening. These analysts can leverage artificial intelligence and machine learning methods, such as deep learning or linear regressions, to predict this. Once the model is created, test data has to be used to assess the predictive power of the model. For models that do not meet the expectations of the bank, they can be fine-tuned to offer higher predictive accuracy.

Data normalization can help increase the accuracy of a data model. Normalization helps achieve greater overall database organization, reduction of redundant data, improved data consistency within the database, and makes database security more manageable.

Once an accurate model is created, it can be a game-changer. Managers only need to feed their normalized data into the models and get the output they need to make decisions for cross-selling in banking.

4. Pay Close Attention To Assumptions

The idea that the future will always mirror the past is a major assumption throughout predictive analytics models. While there is some truth to this, consumer behaviors do change with time. If changes occur to the behavioral assumptions you might have made when creating your predictive analysis models; the models can become invalid.

Also, the variables of the models might change with changing market trends or time. For instance, the financial crisis of 2008 was significantly driven to by the assumption that house prices would always go up, which was not the case. Banks should pay attention to these assumptions to ensure that their predictions are still viable.

Predictive analysis isn’t a silver bullet for achieving cross selling in banking. Not all variables can be predicted to come up with trustworthy insights. Everything from the weather to the country’s political landscape can change buyer behavior. However, predictive analytics offers a much better solution for insightfully allocating marketing dollars than running financial marketing campaigns on underdeveloped research and half-baked ideas. Predictive analytics can provide financial institutions with a much-needed competitive advantage.

Reach out to our team at Insight Financial Marketing today to learn how you can get started with predictive analytics and how to translate changes in customer behavior into opportunities for your business.

 

 

The digital migration is swiftly revolutionizing the way customers buy products and services. Now that digital banking is used by approximately 51 percent of the world’s adult population, financial institutions should focus on creating a sustainable digital marketing program for a fully digital world. This starts by understanding the applicable digital marketing metrics. The following are the six categories of metrics behind digital marketing for financial services:

1. Traffic Metrics

Traffic metrics are mainly measured and monitored during the traffic generation stage. They are very crucial for both SEO and Pay-Per-Click digital marketing strategies. There are several aspects to consider when evaluating traffic metrics, including site traffic and sources of traffic.

Site Traffic

Significant changes in the overall website traffic can give you an insight into how effective a particular digital marketing strategy is. When evaluating the overall site traffic, you should not only focus on the number of page views or hits your site gets, but you should also consider the number of unique visitors your website gets within a specific period. The more unique visitors your website gets, the higher the probability of acquiring potential customers.

Sources Of Traffic

Identifying where your website traffic is generated from and what specific keywords brought them to you can give you an insight on where you should focus your digital marketing campaigns. If search engines are the primary source of the most traffic, you should focus your efforts on SEO marketing. If most traffic is coming from social networking sites, you should focus more on social media marketing, and so forth. Be sure to explore other traffic sources that may prove to be beneficial for your business.

When evaluating the sources of traffic, it is important to assess both the number of mobile and non-mobile website visitors. As more and more people access the internet through their Internet-capable mobile devices, digital marketers must consider mobile traffic an important metric.

2. Engagement Metrics

Is your website content resonating with your website visitors? After reading your content, do they take any action and, if so, how consistently or regularly? Are website visitors downloading white papers and e-brochures or filling out forms?

There are various ways you can evaluate engagement metrics. One of them is by checking the number of clicks your pay-per-click ads receive. Another way is tracking the number of comments, likes, shares, and reposts on social media. You can utilize Google Analytics to track website and app engagement metrics, including page views, unique visitors, and the average time spent on your content.

To boost engagement, you should consider including at least one call-to-action on each of your landing pages, services pages, email, or any other marketing channels that presents a conversion opportunity. You should also review all of your communication channels so you can identify the ones that are generating your desired response. By doing so, you will be able to determine what to change and what to replicate in future digital marketing campaigns.

3. Retention Metrics

Retention metrics are all about establishing whether you are holding your prospects and customers’ attention beyond the initial contact. You should not only check the number of returning website visitors and social media followers, but you should also take note of the bounce-rate, opt-out rate, and the number of unsubscribes.

If the retention numbers outnumber the opt-outs, it’s a good sign that your marketing message is resonating with the target audience. If the retention numbers are decreasing, you should revise your messaging and align it with your target audience’s needs.

4. Conversion Metrics

While getting lots of traffic to your website is an achievement, it won’t mean much if your site visitors remain just that – visitors. The primary purpose of your digital marketing campaign is to convert website traffic into potential customers. As a financial institution, the conversion metrics you should pay attention to are the number of new account openings and new loan applications you get after launching your digital marketing campaigns.

5. Revenue Metrics

The success of your digital marketing campaign can be evaluated appropriately by revenue metrics. You can determine the Return On Investment (ROI) by assessing the website traffic that eventually converted into new business leads or paying customers. By evaluating this metric, you will be able to identify the areas in your digital marketing campaign that are driving sales and revenue.

6. Cost Metrics

This is where you evaluate the amount you spend to launch your marketing campaigns. You have to consider metrics such as the amount you spend on every direct mail campaign you make, every monthly blog post or newsletter you publish, etc. Be sure to determine how each of your marketing efforts is impacting the bottom line, and then use your findings to plan a viable strategy for future digital marketing campaigns and sales cycles.

Data Is An Asset

Everyone agrees that data is one of the most valuable assets any business can have. It’s not the data itself that matters, but what a company does with it. With lots of data at hand, financial institutions have to rethink the way they handle data to be more customer-centric, and, as a result, more profitable.