In today's digital age, businesses have access to a multitude of communication channels. But with so many options, reaching and engaging existing users can feel like navigating a crowded marketplace. This article delves into the power of strategic marketing communication for fostering loyalty, driving up engagement, and ultimately boosting your bottom line.
Communication in this age can mean many things include notification, emails, SMS, calls, even social media posts, home-visit, or meeting. Business communication mainly focuses on the communication when the sender is the business and the destination is the user of this business.
There are mainly 2 types of business communication:
- Operation Communication: it's expected communication, like notify the client about the order state, renewal date, … etc.
- Marketing Communication: it's business related reasons, for example to notify the user about new items added to the business.
While Operation Communication is straight forward, Marketing Communication on the another hand harder and need more resource.
Why Marketing Communication?
There are Major reasons about why we need Marketing communication, but the common thing between them is they are all related to one fact: until this moment, it's easier to reach the existing users than trying to find new users. There are many machine learning model who collect a lot of data about the user to make reaching to new users became much easier but until now the Click and conversion rate for most of these methods not efficient. So how we can expand our service using the user who already pay for this service?
- Cross-selling: users who pay for one service can pay for other service.
- Increase Total amount or frequency: users can pay with higher frequency/amount/tier.
- Increase Retention Rate: users who paid then stop.
- Increase Conversion Rate: users who signed up but never pay any service.
- User Based Marketing: users refer the application.
Communication Plan
Conversion Metrics, How Sender considers the communication as success?
The Most important thing to do is to answer the questions: How can we consider this communication as success or fail? How to measure the target metrics? What can be happened as side effect ?
There are always metrics that business want to improve, like total transaction amount, but there are many metrics that effect this metric. And most time Sender can't hit all sub metrics in the same time, knowing that sender needs to only target smaller metrics to maximize the result in most times.
General Target likes 'payment increased' is something not very practical, Sender need to define some logical Target based on Market and historical data, and this target by its default should be hard but achievable at the same time. Additionally, when sender should measure it is also essential, should business expect the result after a couple of hours or a couple of weeks?
Lastly, business need to be aware of the side metrics, this helps in understanding if the communication has good or bad effect. For example, if communication Z improved the retention rate of users who paid for service X. but there is a side effect that the retention rate for service Y dropped. Should business consider this type of communication as success or fail?
Know The Customers
The key word in successful communication is to know all possible info about the customer, can business have good idea about the user age distribution, gender, finance state, device type, active hours, time zone, ... etc. This information will determine later many things, include how frequent business should communicate with different users, when, which language and channel it should use, ... etc.
The more info the business have, the easier the communication will be, something like when it should communicate, the message's words, and the way of the communication, a lot of these question's answers are variable of who is the user.
Business who hasn't this information will be pushed to the corner and will just use generic approach that will never maximize their matrices.
Communication Bandwidth
Any type of customer has a limited bandwidth, understanding the user base make The communication much more effective. For example, different ages have different resource include time, money, … etc.
No-Cost Communication Mystery
There are many Sender that calculate the Cost of the communication by only its direct cost. So for example something like notification, emails, (and SMS for Telecom company), looks like 0 cost channel they can send as much as they can without any budget constrains.
While this in the first sight looks true, the hidden truth is the cost of the communication should never be measured only by its direct cost, But also with its direct impact. Something like keeping sending unrelated communication to the users make the future cost increase. User who used to receive many Communications will not give them attention. Actually in some time it will block this type of communication, for example user will block the notification, email, or SMS or ignore it, Spam system will filter more of these communications. Finally, the needs of Communication channel with much higher cost will be needed for larger group while it was required for only small group in the normal business.
Who should receive this communication?
Determine who to communicate, how, and when isn't an easy job at all, actually anyone can see companies with large data analysis team to answer this. Even with a lot of data, the Sender will not be 100% sure about the best way to reach the user to maximize the data.
The smaller groups make the communication much effect in most cases. Building data lake that include users profiles and their action history, training advanced models to group slimier groups, identify these groups and find Common things between them, defining the best way to communicate with them, …etc.
All this work need a lot of time and effort but in most cases, it really worth it, it gives the users the feeling this communication came from close person to him which make it harder to ignore.
A/B testing
Defining How to validate the number is also important, someone who claims that after sending notification to user, X of them tends to convert, while some say X is good or bad. My most significant question here is X compared to what? If for example People tends to
convert by Y without notification and Y>X no matter how X is high it
still has bad impact, and this type of communication should be eliminated.
A/B testing will answer this question, Sender will divide the target group to 2 random different groups, and only send to one group of them. Why? Because the Sender needs to avoid the external dependency as much as possible, sometimes external factors (for example a holiday, long term growth, or pricing changes) affect the whole trend much more than the communication. To make sure metrics will only measure the effect of that communication Sender needs to have 2 similar groups, comparing the metrics in both groups give the business much more Insight.
How to divide the main group to 2 similar groups? Simply by assigning people randomly from the main group to one of the 2 groups. Sender shouldn't and should never try to divide the group based on any common things.
Should the size of the first group equal the size of the last ? No, the most simple way is 50-50%, but there are many other ways like 80-20%, it mainly depends on how confidence sender is sure about the experiment, and both groups must have large group of user to ignore random error.
Should we only divide it into 2 groups? No Sender can divide it into more groups, but more groups means the experiment became more complex. Also, you need to be aware of 2 things:
- Control group is mandatory : sender must have a baseline to compare with
- Each group size can't be very small -else random errors will give you wrong result and Sender will never be sure about these result (degree of confidence).
Should we run the test more than once? It's generally good practice to run the experiment more than once, time and events can affect the results, but make sure to adjust the sizes of the groups based on the recent results.
The results of one study -mentioned in references - proved that push notifications sent after A/B testing have a 10% higher click-through rate than those without A/B testing.
References:
Push Notifications CTR, Success Rate and Metrics for 2023
Do Push Notifications increase Conversion Rates?
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