Posts in Strategy

poker

What’s Your Customer’s Next Move?

February 22nd, 2018 Posted by Analytics, Strategy 0 thoughts on “What’s Your Customer’s Next Move?”

You know the #1 rule in marketing, right?

KNOW THY CUSTOMER

And the best marketing campaigns are the ones that are able to get into the minds of their customers. If your message can get the customer to think: “That’s what I was thinking” or “That’s exactly how I feel” or “That’s what I’ve been looking for”, you’ll likely have a winner in your hands.

Therefore, it’s typically for the first step in crafting any ad or marketing campaign is to create your customer avatar. Digital Marketer has shared a nice template, available as a free download here.

John Carlton (“The most respected and ripped off copywriter alive”) frames it as a “Bar conversation” and he talks about it in this podcast. The gist of it is that you’re at a bar, minding you’re own business, and your “Perfect Prospect” walks in and sits down on the stool next to you.

“Hey Susie, line me up a double, I’ve had an awful day.”

“Wow, what happened today?”, Susie, the bartender, replies.

What the person says next holds the keys to crafting your message.

The challenge here is that everybody is unique and may describe their challenges/emotions in different ways. It can be difficult to find the right balance between broad applicability and hyper-personalized, 1-to-1 messaging.

I’d argue that the vast majority of ad spend, particularly brand marketing, is too broad, targeting a large demographic. Companies will create focus groups and talk to a sampling of their target audience to try and have this “Bar conversation” and test the reactions to different campaign messages.

Let me switch gears for a second.

Do you play Poker? As a player, you’re trying to induce your opponent(s) into taking an action (either folding, or betting more – when you believe that you have a stronger hand). Professional poker players know all the odds (the statistical probabilities). They may have also studied their opponents’ tendencies. Poker players will stare down their opponent(s), looking for a “Tell” – an indication of which cards he/she may be holding.

In Texas Hold’em, you have information in the form of your cards, the community cards, any previous actions (bets). This is basic information.

A player could shift the odds tremendously in their favor if they just knew ONE of the cards that their opponent held. Actually, they could shift the odds by just knowing a couple cards that aren’t in play (i.e., what other players folded or what’s in the deck).

The main point is that these types of players are actively seeking additional information about their opponent(s) so that they can make smarter decisions (to induce an action).

Now, to switch back to marketing.

The poker analogy I just described explains how having more information about your customer, empowers you to make better decisions to induce them. That’s what analytics and being data-driven is all about, right? It’s using data to support a decision.

Would it help your marketing initiatives if you knew, for example:

  • Where the customer is in the buyer’s journey (awareness, consideration, decision)?
  • The probability that the customer will buy Jeans vs. a Blouse?
  • The customer is exhibiting the same behaviors as thousands before them who eventually bought something?
  • Those customers coming to the site from the “Facebook Free Trial” campaign are showing a higher propensity to subscribe?
  • The likelihood for the customer to call and ask to cancel their subscription?

 

You don’t have to create focus groups for this because your customers are talking to you every day. They are communicating to you through their actions and their behaviors. Every time someone visits your site or opens your app, they are leaving breadcrumbs, feeding your clickstream data.

Machine Learning can surface customer information in the form of Algorithmic Customer Segmentation, Product Recommendations, Propensity Scoring, Attribution Modeling, Churn Analysis, and so much more. Furthermore, it’s AI-Assisted Customer Analytics which uses Machine Learning to discover the information you need about your customers so that you can make smarter decisions for your business.

With today’s technologies, this is all low-hanging fruit. You don’t need a large team to implement AI and you can have it delivered in weeks. Yes, it’s absolutely possible.

Just don’t forget that behaviors change all the time. You can’t set it and forget it. It’s AI-Assisted so humans are required!

 


Kerry Hew

Kerry Hew is an Account Executive at Syntasa. He loves sharing ideas with other curious and growth-minded Digital Marketers and Data Scientists alike, and working together to execute on their visions faster. Connect with him on LinkedIn.

data ownership

Who Owns the Data?

January 30th, 2018 Posted by Strategy 0 thoughts on “Who Owns the Data?”

It’s a tale as old as mankind. When a precious new resource is discovered, groups of people will try to stake their claim or debate over who the rightful owner should be. Data ownership is no different. Every day companies handling large amounts of data are grappling with this. Often times, the day-to-day handling of data is carried out by two, if not three, teams: IT, analytics, and sometimes marketing.

IT is the necessary safe keeper. Without a team working consistently and creatively to secure the data, the company is vulnerable to phishing attacks, malware, ransomware, etc. In addition, IT usually handles data storage, though that could change as companies become more interested in finding new uses for their data.

Customer data analytics is the star of the cast, developing applications for the data. Over time, these applications have become more and more sophisticated, employing new self-service and user-friendly tools such as AI assisted customer analytics to activate the data in real-time as customers shop online. These teams are relatively new but have become essential to a company’s long-term competitiveness.

Marketing is one of the chief beneficiaries of a company’s data infrastructure. Generally, these teams will use the insights gained from analyzing its data to create more sophisticated marketing campaigns. For example, discovering what attracted, retained or repelled customers can help the company better serve them and achieve enormous gains. This is why marketing departments across many different industries are being transformed by the emergence of big data.

In order for the data to be used optimally, it’s essential for these teams to collaborate and define the analytics which will benefit each business group. Because just as in a good play, each business group has a different (but equally important) role in contributing to the business’ success.

How does your company manage ownership over the data? Tweet at me or email me to share your thoughts and experiences.

 

chief data officer

6 Tips for A New Chief Data Officer

September 29th, 2017 Posted by Executive, Strategy 0 thoughts on “6 Tips for A New Chief Data Officer”

A guy walks into a bar. The bartender asks, “What’ll it be today?” The guy answers: “Gee, I just don’t know.” Pretty boring joke, right? The same letdown occurs when a new Chief Data Officer has no clear strategy for the future.

The title of Chief Data Officer has only emerged in the past few years and most CDOs today are the very first to be filling that role at their company. And because they have no shoes to fill, they tend to walk in barefoot. That is, they come in without any overarching strategy for how the company’s data should be stored, shared and exploited. BIG MISTAKE, because the earlier a company develops its data strategy, the more valuable its data will become in the long-term.

Here are 6 tips for an incoming CDO:

1. Think outside the box. Every day, companies are finding new ways to slice and dice data in a valuable way. For instance, internal data like inventory are now helping companies become more efficient. The CDO should make sure to survey all the departments to find out what kind of data is out there, just waiting to be activated.

2. Develop a working plan to define the ways in which data can unlock business outcomes. Don’t set a 1 year plan, let alone a 5- or 10-year plan as this will only get changed multiple times within the first year.

3. Determine who is in charge of what data. That could be several teams – for instance, IT could be in charge of securing it, and Data Analytics in charge of building apps over the data lake.

4. Ensure the company has a logical map for the data. The data lake is an incredibly rich resource, and why it needs to be used smartly. A clear data architecture will save time and money and facilitate the creation of apps.

5. Define a strategy for apps. This should be a central concern for the CDO. After all, the data on its own is like silver in the mine. It is useless until it gets activated. From the outset, the CDO should have a strategy on the types of apps the company should develop versus buy.

6. Start small, scale fast. Find a dataset that is easy enough to pull insights from, such as clickstream (or event) data. The company can leverage this kind of data very quickly, especially with predictive behavioral analytics.

 

If any of this resonates with you, tweet at me or email me to share your thoughts and experience with, or as, a CDO.

 

data

Data That Stays Together, Works Together

July 31st, 2017 Posted by Strategy 0 thoughts on “Data That Stays Together, Works Together”

When it comes to data analytics, marketers are missing the forest for the trees. If you think your company’s most marketable data source lies in your enterprise data, think again. Your company is sitting on a gold mine of customer data, siloed in different departments, just waiting to be integrated and activated.

According to eConsultancy’s 2017 Digital Intelligence Briefing on Digital Trends, 59% of marketers who have an intermediate or advanced understanding of the customer journey stated that they had trouble unifying different data sources.

On the front end, you may have your clickstream data, which can include activity from ad displays, social media and email campaigns. Some companies even have data on the voices of their customers and that’s a real trove for piecing together customer demographic profiles. Companies also have loads of data on the back end, waiting to be mined, and this includes margin data, CRM product data, and enterprise resource planning, among others. Combined, both front end and back end data can turbocharge your data analytics system.

But in order for this to happen, the data needs to be removed from its silo and made accessible in a central behavioral data repository. Everything under one roof and one program to rule them all.

A predictive behavioral analytics platform can take almost any type of data sitting in your data lake and turn it into gold. It does not require you to manually identify every single data point across different departments because machine learning algorithms do the work for you. An individual data point teaches the model something completely new, regardless if it is tied to other data points in the dataset or not. As the number of data points coming in to the central behavioral data repository grows, the algorithm’s predictions on user behaviors become more and more accurate. Therefore, companies which have started activating their data are gaining the edge needed to secure their spot as a market leader for tomorrow. The sooner you “compound”, the greater the benefit.

Are you already doing something similar? Tweet at me or email me to share your experiences.

 

analytics applications

Are Homemade Predictive Behavioral Analytics Applications Better?

July 17th, 2017 Posted by Analytics, Strategy 0 thoughts on “Are Homemade Predictive Behavioral Analytics Applications Better?”

Homemade apple pie beats the Entenmann’s variety, right? Well, only if there’s a good cook at the helm. The same principle holds true when building analytics applications. With an advanced analytics solution, IT teams can, for example, tailor their websites to a visitor in-real-time, delivering the right content at the right time. And your team may be perfectly capable of building an infrastructure and enterprise application.

But what happens when you have to build for a more specialized function, such as predictive behavioral analytics? More often than not, the necessary skill sets for building these kinds of applications fall outside the scope of your team. You’ll know at the time of deployment just how tall this order will be for your company’s IT coder. They will declare victory after they have created a number of scripts, yet still there is no product.

That’s why large organizations – including government agencies – get an advanced behavioral analytics solution to sit on top of their open-source big data stack. It’s that lattice top that every homemade apple pie needs. In this way, organizations have complete control over their data, proprietary models and implementation. There is no confidential or proprietary data that ever needs to be shared with an outside vendor. And with an open-source framework, your data scientists have the freedom to build models on top of it if they so wish.

So, the question is which applications should you build in-house and which you should buy. Enterprise analytics applications are designed for enterprise IT to build in-house, whereas predictive behavioral analytics applications are acquired, as long as it is the right one – one that can sit on your data lake and is not a “black box” of proprietary technology.

Do you have a similar experience? Tweet at me or email me.

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