Breaking - Marketing Automation

What is Marketing Automation?

A combination of tools & thinking to personalize the journey of prospects and make them happy customers.

Why do you need it?

To identify the right leads, nurture them, move them faster in the funnel, generate revenue & deliver better ROI on campaigns. 

Email marketing & landing page development

Sending emails and getting people to land on designated pages using a CTA to help them convert is the essence of this practice. 

Lead Management (Capturing, Scoring & Nurturing)

Lead Capturing: Leads can come from various places like websites, social media posts, email lists, campaigns, etc. These leads should ideally land in the marketing automation tool automatically with the help of an API.

Lead Scoring: By assigning values to basis some predetermined rules on behavior (on website, social) or demographics and give predictive scoring by connecting CRM system as well.

Lead Nurturing: To keep leads engaged thru personalized campaigns.

CRM Integration

Most businesses need to align sales and marketing and the need for CRM integration has become a must for marketing automation. Platforms like Salesforce, Microsoft Dynamics, Oracle Netsuite & Sugar CRM are  9/10 times available as connectors.

Sales team enters leads and the marketing automation platform gets those leads automatically to be acted upon.

Apps Marketplaces

Use of third party helps to free up time for other marketing activities. Apps for content creation, benchmarking apps, SEO ranks, etc. Some time with the use of an API these connections can be made which might incur a cost per call.

Dynamic content creation 

Ability to create, send & measure personalized email campaigns. Some platforms have better personalization capabilities in terms of content creation on websites or emails. It also differs in static & dynamic content which adjusts as people interact with it. Use of Progressive profiling to capture additional information from a prospect each time they fill something.

Even email & message deliverability monitoring and dedicated IP for the same is useful in marketing automation platforms.

Account-Based Marketing

B2B selling requires marketers to reach out to multiple stakeholders in an organization, which makes ABM essential for a complex high-value sale. Hence enhanced account nurturing and predictive scoring come in handy in such cases. 

Mobile Marketing

Since mobile is now at the heart of most campaigns many marketing automation tools offer responsive templates for email, landing pages & webforms. There are email testing tools that help in getting a preview of the same across platforms.

In-App marketing, push notifications, Geo-fencing are increasingly deployed to get prospects to share of mind.

Predictive Analytics

Most marketing platforms offer standard analytics like clickstream data & campaign responses. Most platforms are offering predictive models that give trends and insights to enhance the customer experience. Advanced platforms also recommend the next best action - product recommendation or website content basis the prospect interaction with brand assets like Website, CRM, etc.

Social Integration

Almost every platform will have some method of publishing & monitoring on social networks from within. Some will have advanced features to monitor the social behavior of leads and scoring them.     

How to measure the success of marketing automation

It is also wise to check data before getting started on the journey to email automation. Also, be mindful on synergies of email with other campaigns on performance. 

Some of the most popular platforms:

Salesforce Pardot
Hubspot
Marketo
Eloqua
Mailchimp

Breaking MMM vs MTA vs CCA

Marketing Mix Modelling vs Multi-Touch Attribution vs Cross Channel Attribution 

Why do you need MMM or MTA?

Every marketer needs to know where to spend their budget to get maximum returns. There are different methods/models in analytics and data science to help the marketer makes sense of there spend vs returns.

Let's try and break them one by one.

Marketing Mix Modelling (Media Mix Modelling)

Analyses most of the offline data like TV, Print & radio & gives a high-level picture of what is working and to invest. It takes into account a holistic view of the market environment like the price, seasonality, weather etc. Basically the effect of these factors on the performance of any marketing campaign. This is mostly done couple of time a year max.

Multi-Touch Attribution

MTA takes more of a ground-up approach as it looks at user journeys and tries to ascertain their conversion paths. Every touchpoint in the user journey is assigned a specific weight and feeds into the MTA analysis. It is more of a real-time digital play when compared to MMM which is mostly historical in nature.

Let's look at the Pros & Cons of both approaches:

MMM - Pros

1. Macro-level & strategic in nature.
2. Financial implications of spend (ROI)
3. Longer time frame & context for the brand

MMM- Cons

1. Not real-time hence delays optimization
2. High level hence difficult to go deeper into specifics of user journeys

MTA - Pros
1. Quick response is possible
2. Helps understand which channels work best together & what does not

MTA - Cons
1. Misses offline as the most emphasis on digital
2. Baseline conversions which happen without any marketing are missed
3. Better than last-click attribution but still restricted to one algorithm

Due to this disconnect between both MMM & MTA are run at different times to gauge overall marketing.

Cross-Channel Attribution   

If we use a common key like the time we can stitch together both MTA & MMM. Using advanced analytics to allocate proportional credit to each touchpoint across be it online or offline channel, leading to the desired customer action.

Breaking - Datalake & Data Warehouse

What is a Data Lake & how is it different from a Data Warehouse?

Enterprises have loads of data which is often very varied and difficult to make sense of. A Data lake keeps that data in its purest form which can be used for different purposes. 

Traditionally Data warehouses have helped us use the data that we have but in the age of Big Data, this model doesn't serve us well. 

Data Warehouse

We take data from structured data sources, do some ETL and structuring and basis a predefined data model create data marts for reporting, OLAP cubes for slicing & dicing & visualization. 

This process needs the understanding of the data that is coming in (Source, Type, Anomalies, Cardinality) along with the business requirement to make it work. 

Also based on the fact that the business understands the requirements. So most of the time goes into understanding what, where and how of data than on actual analysis. 

Data Lake

Here the data sources can be structured or unstructured. We extract and load all types of data to a raw data storage. This place is a persistent storage that can store data at scale (Volume).

Components of a data lake:

1. A sandbox environment: For understanding & exploring data, creating prototypes & use cases. 
2. A batch processing engine: For converting raw data into structured data used for reporting.
3. Real-time processing: Handles streaming data & processes it as well.    
4. Cataloging & Curating: The value of data basis its source, quality & lineage.Helps in deciding which data set to be used for a particular analysis. Helps in providing Meta-data.

Lambda architecture: The hybrid approach of batch & Real-time processing is called Lambda. Batch layer is the slow layer whereas the Speed layer takes care of fast incoming data. Once data is passed through the speed layer it goes for batch processing.

This makes room for more data to come in the speed layer. The batch layer & the Speed layer run together when queried for reporting.

Glossary

ETL: Extract, Transform, Load (ETL) first extracts the data from a pool of data sources, which are typically transactional databases. The data is held in a temporary staging database. Transformation operations are then performed, to structure and convert the data into a suitable form for the target data warehouse system. The structured data is then loaded into the warehouse, ready for analysis.

Persistent storage: Persistent storage means making data available even when power is off. Like a hard disk. The volume, requirement of availability & distributed compute makes it complex storage. 

Batch processing: Typically uses Hadoop MapReduce 

Immutable data: That cannot be changed once stored.

Master data set: This is where all batch process data is stored. This data is immutable.

Atomic data: That data which cannot be broken further. Not calculated metrics like revenue.

Timestamp: Recording event information called log for organizing data.