This final article in the series on marketing transformation focuses on the technologies that can support your marketing transformation through data. The targeted use cases are as follows:
- Measurement of marketing activity performance
- Generation of insights about customers and consumers
- Use of these insights to:
- acquire new customers and prospects
- increase the value of current customers
- retain high-value customers
- Personalization of customer experience
Every marketer needs to have a 360-degree view of their customers to develop and execute effective strategies. Obtaining this overarching viewpoint is achieved by creating a marketing customer information file (or MCIF) which contains a list of relevant attributes to support the applications listed above.
Creating a 360 view of the consumer is not a recent idea. This process has been in existence for around twenty years in the form of the intermediary data marts for marketing that underlie organizations’ marketing campaigns. The growth of digital and the increasing accessibility of cloud-based platforms have enabled the acceleration of this type of project (through data unification). Data from multiple sources (that generally exist in silos) must be identified, extracted, warehoused and analyzed to achieve this 360-degree view. They are then activated to generate value for the organization.
In today’s business world, to be able to compete in the digital ecosystem, a vendor of technological solutions must provide a platform rather than a product (software). Platforms differ from products in that they allow services, functionalities and applications to be built around them, while a product generally only delivers a single service (an application). Platforms are composed of a catalogue of APIs that enable data to be exchanged with other systems or the construction of services or applications on top of said data (there are APIs for importing, exporting, activation, configuration, utilization). All the web giants started out with products and, over time, transformed these products into platforms. As an example, here is a (non-exhaustive) list of vendors who have made this transition: Google Cloud Platform, Google Marketing Platform, Amazon Web Services, Azure (Microsoft), Adobe, Facebook, etc.
One of the key properties of a data platform is its ability to integrate into your current ecosystem. Cloud-based technologies have greatly facilitated this kind of integration.
According to Scott Brinker (chiefmartec.com), there are currently an estimated 7,000 sellers of MarTech in the market. To better understand the role of these technologies, you need to understand the data life cycle, which is composed of several phases:
- Collection or ingestion
- Storage
- Analysis
- Activation or use
Each of these phases is supported by different kinds of technologies. It’s clear that a single provider can rarely fulfill all of an organization’s marketing needs. The reality is that a lot of orgs have multiple cloud-based solutions (combining several cloud or MarTech platforms). It’s possible, for example, to combine Adobe and Google, Azure and Google, Salesforce and Google, etc. The best-of-breed concept is what often guides decisions when it comes to selecting technologies.
The table below is a summary of the kinds of technologies supporting the data life cycle:
Collection and ingestion of data
The collection and ingestion of data marks the start of the value chain. This stage is supported by a measurement plan (tracking) which is aligned with the organization’s business objectives. In this stage the key elements to be collected are identified.
There are two modes of data ingestion: streaming and batch uploads. Streaming is generally related to collecting events in real time, while batch uploads involve uploading files (CSV, JSON, PARQUET, TXT) to storage platforms at a predetermined rate (hourly, daily, weekly, monthly).
Here are a few platforms to keep in mind for this phase:
- Google Tag Manager (GTM) and Adobe Launch, which manage the collection of web and mobile event tags (focusing on marketing and digital analytics);
- Google Cloud Pub/Sub, Azure Event Hubs, Confluent Kafka, Amazon Kinesis Data Streams, which are a lot closer to traditional event streaming (operational analytics and IoT);
- Supermetrics and Fivetran, for integrating advertising and marketing data into storage platforms;
- Google Dataflow and Datafusion, Azure Data Factory and AWS Glue, which enable the extraction, transformation and loading (ETL) of data into centralized repositories.
Data storage
Storing various internal and external data is required for unification purposes. Generally speaking, unification solutions are required to build a customer marketing profile. When it comes to data storage, there are several architecture types available, the most popular being the data warehouse, the data lake and the data lakehouse, which is a hybrid between a warehouse and a lake. Data lakes are composed of unstructured data (images, videos, etc.), data warehouses are composed of structured and semi-structured data (transactions, customer profile, etc.) and data lakehouses can manage both types of data.
The most popular data storage solutions (data clouds) are:
- Google BigQuery, a Google product;
- Snowflake, a platform that can be installed on Azure, AWS and Google Cloud;
- Amazon Redshift on AWS;
- Azure Synapse Analytics from Microsoft.
Data analysis
This phase is critical for transforming data into business intelligence (insights). The main tasks covered in this section aim to identify valuable customers, their behaviours, their socio-demographic attributes, and their needs and attitudes relating to the brand. The final objective is to produce a 360-degree profile of the customer, similar to a customer marketing profile. These profiles are derived from digital audiences and are then sent to advertising, email marketing and personalization platforms. Advanced analytics (predictive and prescriptive) are also used to predict events such as the probability that a prospect will become a customer or that a current customer will make another purchase.
Here are a few popular tools and languages for data analysis:
- SQL
- Python
- R
- Google BigQuery ML
- Mode
Data activation
Value generation cannot be completed without the activation of data. The main data applications were listed at the start of this article.
Here are a few technologies that support data activation:
- Visualization: Tableau, Power BI, Google Data Studio
- Advertising platforms: Google Ads, Display and Video 360, Facebook Ads, LinkedIn Ads
- Email marketing platforms: Salesforce Marketing Cloud, MailChimp, Adobe Campaign, Klaviyo
- Experimentation and personalization platforms: Adobe Target, Google Optimize, Optimizely
Customer Data Platforms (CDP)
Customer data platforms have been in vogue for a few years. A lot of vendors present them as a solution for all your data unification problems where you’re trying to create a 360-degree view of the customer (especially given the disappearance of third-party cookies). Certain sellers will even go so far as to present them as tools that can be used as a data warehouse or lake. But honestly, CDPs are not data warehouses and cannot replace a marketing data warehouse. Launching a CDP initiative without a data strategy is a risky operation. As with any technology, they require specific skills, a well-defined strategy and time-tested processes. Don’t hesitate to get in touch with the team at Adviso to learn more about this subject.
GA4 and Google BigQuery: New opportunities
By making the granular behavioural data of GA4 (website and mobile app) available to marketers free of charge through its cloud-based platform BigQuery (before GA4, you would have to spend US$150,000 for access to these data), Google is positioning itself as an accelerator of marketing transformation through data. Many organizations can now afford to unify their digital data with primary internal data such as those from CRM, product catalogues, call centres, customer feedback, etc. This strategy will enable Google to position itself as an essential player in the construction of a 360 view of the customer, which is indispensable for successful digital transformation.
I hope you have enjoyed this series of articles. If you would like to learn how to begin accelerating your construction of a 360-degree view of your customers don’t hesitate to get in touch with us.
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