Understanding the Impact of CCPA on your Digital Presence

Privacy in the digital era has become a hot topic. Headlines about data breaches, fake news, and even election tampering have become part of our day-to-day as we continue to consume and share content on social media sites. After many years of inaction, government entities are beginning to enact legislation to improve protections for internet users, starting with the landmark General Data Protection Regulation (GDPR) passed by the EU. For marketers, understanding the impact of these new regulations can be confusing and complex.

In 2018 California adopted a series of consumer privacy regulations collectively known as the California Consumer Privacy Act of 2018 (CCPA). These laws intend to strengthen consumer protections including but not limited to the use of data collected by internet services, including Social Media, Search Engines, Advertising technology, and internet connected devices. The CCPA will go into effect as of January 1, 2020.

While EU companies are subject to the more narrowly defined General Data Protection Requirements (GDPR) which also went into effect in 2018, the CCPA represents the strongest internet privacy law currently in place in the United States. With most internet platform providers located in California, this effectively governs consumer data protection nationwide, and affects platforms such as Google, Facebook, LinkedIn, as well as most programmatic advertising platforms.

With the explosion in popularity of digital media, consumer data has proliferated across a multitude of platforms and data services. Since provision of consumer data is often requisite to the usage of these platforms, consumers often are required to give access to potentially sensitive data in order to access common features such as email, or to engage with others on a social media platform.

This data is often stored, sold, packaged and distributed across first and third-parties, often for ad targeting and content personalization. Most internet service providers rely on revenue generated by selling this data to advertisers. Prior to the CCPA, little-to-no regulation exists to govern the storage, sharing, and distribution of consumer data. Additionally, it is difficult for consumers to control this data, view what data is stored, grant or revoke access to it, or trace the data through third parties.

The CCPA sets out to define clear rules on how consumer data may be stored, gives consumers the right to obtain clear visibility into data collected about them, and the right to control how that data is used for commercial purposes CCPA also defines the compliance framework for business which collect and store consumer data.

Key Provisions:
Right to know what Consumer data is being collected
Consumers must be able to request and obtain data being collected about them; companies must disclose what data they are collecting upon request.

Right to know what Consumer data is being sold, and to Whom
Consumers must be able to request and obtain whether the data being collected about them is being sold; companies must disclose to whom consumer data is being sold upon request.

Right to say no to Consumer data being sold
Consumers shall have the right to direct a company not to sell data being collected about them; companies shall comply upon request.

Right to Equal Service and Price
Companies may not restrict or modify services based on a Consumer’s request to obtain or restrict Consumer data. Companies may not charge fees to Consumers in order to request or obtain Consumer data.

Companies may be subject to civil suit for damages, as well as fines ranging from $1,000 – $3,000 per incident.

For most companies with a typical corporate web presence, first-party Consumer data collection is limited. Most internet usage data collected on a commercial web site is anonymous and not personally-identifiable, and most companies rely on third-party data platforms such as Google Analytics to collect Consumer data, and this data is not stored with the Company. Data platforms are required to provide obvious links consumers can use to make requests for their data, as well as provide the appropriate disclaimers.

In the event a business self-hosts an application which collects first-party consumer data, the company must be in compliance with the provisions above.

The main effects being:

An easily visible link or toll-free phone number which allows Consumers to request their data or direct the company not to sell it.
Updates to the terms-of-service for the web site or application disclosing compliance with the CCPA.
Internal processes to facilitate the retrieval and response to Consumer data requests.
While many marketers may avoid negative impacts due to these new regulations, it’s critical to understand the impacts. Compliance may be simply a matter of following simple guidelines, updating legalese, and implementing common-sense data governance rules, and savvy marketers will be able to continue executing on their digital programs without risk of running against these regulations.

Matthew Lee is President of Motionstrand, a Digital Customer Experience Agency based in North San Diego County. Motionstrand works with brand, media and client partners to deliver exceptional CX for the Healthcare, Pharma, and Medical Device space.

What is Apache Cassandra?

Apache Cassandra is a distributed, highly scalable,  high performance NoSQL database.  It offers several advantages over traditional relational database management systems (RDBMS), particularly in write-intensive, globally distributed, high availability situations that span geographies and datacenters.

Apache Cassandra is Open Source and distributed under the Apache 2.0 license.  Originally developed by Facebook, Cassandra is used by many global enterprises including Apple, Cisco, and Netflix.

Unlike traditional RDBMS systems, Apache Cassandra is a NoSQL database.  Rather than relying on related tables to describe data, Cassandra uses a simplified data storage architecture known as ‘wide column’.  This allows for the simplicity of key value storage, but row data types can vary per row, allowing for the flexibility of tabular data storage.  Cassandra also has the concept of ‘Column Families’ which allow grouping of columns into tables, but rows do not all need to contain the same columns.  Key / Value storage is fundamental to NoSQL databases as they allow for fast indexing, writes, and retrieval.

NoSQL databases forego complex transactions and guaranteed consistency in favor of a highly scalable, strongly or eventually consistent model which is well-suited to internet-scale applications.

Apache Cassandra is masterless, meaning that all nodes in a Cassandra cluster are active and communicating with each other. Any node in the cluster can accept and serve requests, and in the event of a failure to a given node, traffic can be automatically redirected to another active node with no need for complex master – slave replication schemes.  Cassandra automatically distributes and maintains data across the cluster with no need for complex sharding and disk partitioning.

Additionally, Cassandra’s replication approach is much simpler than multi-master or master-slave architectures.  Once a replication schema is created, it is automatically managed across all nodes of the cluster without need for any additional administration.  

Cassandra also exposes an SQL-type query and management interface called Cassandra Query Language (CQL) which allows for developers and administrators to interface the system using familiar RDBMS queries.

Why use Apache Cassandra?

Due to it’s highly distributed and fault tolerant nature, Apache Cassandra is well-suited to globally distributed, write and read-intensive applications that require high availability and high scalability.   A few examples include Social Media data, IOT Sensor data, User Tracking and Messaging applications.

A common reason to use Apache Cassandra is to locate highly available database clusters close to end users in a globally available application.  Since Cassandra nodes can be replicated across any type of infrastructure, including private, public, and hybrid clouds, Cassandra is well suited to geographically distributed applications.   Reads and writes can be delivered with low latency, close to the end user, and replicated throughout the cluster from any node.  This is especially important in high throughput scenarios where locating data infrastructure close to the end user can result in a significant reduction in bandwidth costs.

Additionally, Apache Cassandra is well suited for applications that may require significant scaling up or down.  Adding and removing nodes in a Cassandra cluster is simple and requires no downtime.

What is a NoSQL Database?

Relational Database Systems (RDBMS) are the traditional software platform for storing, indexing, querying and analyzing data.  Relational Databases expose data as tables, where data can be grouped into rows, with columns defining the datatype to be stored, such as numeric, text, or binary data.

RDBMS can use relationships between tables to define a data schema which models the real-world business case. These relationships can define reference data. For example, it is common to store a list of countries, cities, and states in separate tables, and use references, known as keys, to link these lists together to create a final definition for a location. These references can be enforced via a function called ‘referential integrity’, which ensures that a new record cannot be created unless the references to other tables are also created at the same time.

These features are designed to accommodate a set of standard properties known as ACID, or Atomicity, Consistency, Isolation, Durability.  These features allow the database to process transactions which can be rolled back in the case of a system failure, ensuring a transaction can never be in an incomplete state.  Financial transactions are typically ACID-compliant.

Querying an RDBMS is accomplished via Structured Query Language (SQL), a simplified English-like programming language that allows retrieval, update, delete and manipulation of data.  SQL queries can also be used to manage the database system itself, including backups, recovery, physical storage and performance tuning.

A NoSQL database is similar to an RDBMS because it allows for data storage and indexing, however it eliminates or reduces many of the features in an RDBMS.   NoSQL databases typically use a simplified table design, often simply key-value pairs, does not enforce referential integrity, does not offer ACID-compliance or often any transaction processing and rollback capability.   And NoSQL databases typically do away with SQL in favor of a simple API, often exposed as a REST service.  NoSQL databases use a Key / Value pair as their primary storage mechanism, eliminating the need for multiple tables, joins, and keys, and simplifying the underlying data storage.

Why use a NoSQL Database?

Many applications do not require the overhead of an RDBMS and don’t take advantage of the features they offer.  These applications often are non-transactional, read-intensive, widely distributed, and deal with unstructured data.     As an example, social media posting is a good candidate for a NoSQL database, and indeed many Social Media properties rely on NoSQL databases as their primary approach for data storage.

In the common use cases of text storage, image storage and retrieval, a NoSQL database will often outperform an RDBMS at scale, due to the simplified nature of storage underlying a NoSQL database.   In applications where fast retrieval is important, this can be a critical feature.    In a clustered environment, NoSQL databases can often get significant performance improvement over an RDBMS by relying on ‘eventual consistency’, where data may be updated to child nodes over time, rather than needing to be updated immediately during a transaction.

NoSQL databases also makes it simple for developers to add persistent storage to their application.  Since data management operations are exposed via APIs, there is no need for a developer to embed or expose SQL into their code.  NoSQL databases also map much more closely to the object-oriented programming paradigm, which relies on simple, self-contained key value data schemas comprised of fields as opposed to table structures.

Any developer looking for a highly scalable, fast and simple database should take a good look at a NoSQL database.   If the application does not require the transaction processing features of an RDBMS, a NoSQL database may be a more simpler, more scalable approach.

B2B Digital Strategies for Medical Device Manufacturers

Device manufacturers often struggle to grow awareness as they introduce new products to market.  Educating healthcare practitioners and patients can be a complex, time-consuming, and expensive process.  Despite rapid advances in sales and marketing tools, most device manufacturers stick to the tried-and-true tactics such as field sales, trade shows, and brochure-ware web presence.

Over the past few years, there have been significant changes in B2B sales approach, particularly the rise of account-based-marketing (ABM) strategies that use CRM, digital and third-party data to help focus on marketing into specific customer accounts, and often rely on tools that are able to identify engagement across a company and it’s multiple decision makers.  Artificial Intelligence (AI) and Machine Learning systems have proven the ability to automate away the complex process of  identifying and engaging with the target market.

I’ve identified some of the common strategic areas that can help you quickly and effectively determine the right tools, tactics and messaging for your medical device campaigns.

Measurement Matters
Understanding how to measure performance is a key first step to any successful digital program.  Determining the goals for the campaign and how it will be measured helps create alignment with sales, marketing and executive management, and ensures marketers don’t end up talking about ad impressions and brand reach while the CEO asks tough questions like “how much money did we make on this campaign?”
For most marketers in the medical device space, the focus is on conversions and the cost metrics around customer acquisition.  Determining return-on-investment (ROI) for marketing campaigns and channels is often complex and requires multiple data sources including CRM and ad-tech platforms, but will help deliver a clear picture of the value that a campaign brings to the business.   Most digital marketing platforms do a great job at measuring basic advertising metrics like impressions, engagement, and reach, but measuring ROI often requires marketers to go the extra mile and integrate their sales data to tell a real business story.

Build an Audience, then Activate it
One of the biggest shifts in marketing has been the declining effectiveness of many traditional advertising tactics and the shift to social media and content marketing. More than ever, consumers of all types, including corporate buyers, expect to learn about new products in high-trust environments, particularly those that contain some sort of peer validation, as opposed to advertisements.   Customers expect brands to deliver significant value in the marketing and sales process. Digital excels in this area, and there are a number of approaches that can help a brand build an audience of interested prospects and influencers without using often-wasteful mass advertising tactics.

Educational content marketing is particularly effective in new markets where demand may not yet be fully realized, particularly for emerging product categories or new technical break throughs. Device manufacturers can often own demand for information through some basic search-engine marketing techniques.   By owning the educational content for a category, the manufacturer can build and capture demand for the search terms around the category, and grow awareness as well as traffic back to the corporate site.   

The most common and successful approach for many brands is building an audience through value-add content marketing.  With this approach, the brand produces a high value content piece which requires prospective readers to give their email address to obtain a copy.  Once limited just to newsletters and email blasts, this category has become increasingly sophisticated to include vendor pricing and selection guides, salary surveys, how-to content, industry surveys, analysis and insights are all content items used by brands to help build their in-house lists.  Setting up customer journeys, drip campaigns and newsletters are all popular ways to keep prospective leads engaged and encourage them to convert.

Impact and Opportunity: Zero Click Results in Pharmaceutical Search

Over the past several years, Google has increasingly devoted screen real estate on their search engine results page (SERP) to reflect a mix of Google’s ad products and auto-generated content known as rich snippets and knowledge panels. In many cases, these Google-owned content items dominate the screen, pushing organic, corporate and brand web site listings down below the fold.

The net effect of this has meant fewer clicks to brand sites, in fact in many cases, no click at all. Google users can now find the answer they are looking for in these knowledge panels, and many never need to click through to a web site. According to a SparkToro/Jumpshot survey from 2019, 50% of all searches fall into this category, and estimates for the healthcare industry are even higher.

For pharmaceutical companies trying to tightly control their brand and claims, and manage their content under regulatory scrutiny, these so-called ‘zero click’ searches represent a key opportunity to ensure that a brand and product is well-represented in what is often a high-intent digital moment.

Studies have shown that users trust the knowledge panels, click on them at higher rates, and that user experience satisfaction is high. If your brand’s goal is to be associated as an authority on a disease state, drug facts or brand information, it’s important to understand the impact and opportunity of zero-click results.

This automated content is part of Google’s Knowledge Graph, a database which uses structured data to define the context of internet data and content. It obtains this from a number of first and third party sources. This database classifies content using data structures, also known as schemas, to create complex references and relationships between all sorts of things, including a rich set of schemas for the healthcare, medical, and pharmaceutical industries. You can see an open-source library of definitions at https://www.schema.org/.

Google’s bots use this structure to classify and link data through context. Schema definitions can be inserted as XML snippets into the code of a web site, often called a ‘rich snippet’, and Google will look for them as they index the site.

There are dozens of rich snippet types out there, but specific to pharmaceutical and disease state searches, there are several key knowledge panels we want to get familiar with. Google has a huge amount of data it has obtained by indexing a large amount of medical information. Relatively few sites take advantage of schema markup and rich snippets, even as health-related searching has skyrocketed in recent months, and is anticipated to continue to grow.

Health Fact Featured Snippet Card

This smaller card essentially will appear in what is effectively the #1 page position. It is usually reserved for high volume searches on a topic. Content publishers such as WebMD often own this card. These cards generally define clear topic, category, or item.

Medical Fact Knowledge Panel: 

This panel is typically displayed when a specific disease, condition or disease state term is searched. Disease state panels can be very robust, and often link to related diseases and conditions.They also often cross-reference drug fact panels. Especially for rare diseases, displaying a knowledge panel for this result should be a priority. These panels can be very robust with related facts, cross-references to other knowledge panels, and many other valuable links. Note that Google employs a staff of medical artists to illustrate these knowledge panels.

Drug Fact Knowledge Panel

This panel typically is displayed when a specific drug or brand name is searched, and will display a variety of auto-generated information about the drug, it’s indications, FDA status and much more. Getting a knowledge panel result for your brand terms is ideal. Basic panels will contain minimal information, while the more robust panel example below has related searches and cross-references other medical fact panels.

Q&A Knowledge Panel

This is the most common knowledge panel and it attempts to answer commonly searched questions. Many sites will implement Q&A rich snippets to attempt to own this space. Often you may find dosing, side effects and claims being answered through these automated Q&A snippets. Owning these results should be doable and is generally less difficult than the drug fact and disease fact knowledge panels. Rich snippets included in Q&A panels can be datatables, numbered lists, related links, and much more.

Common Knowledge Panel Sources

Google relies on a variety of first and third party data sources, favoring a mix of government agencies and commercial data portals. Ensuring that your brand and product is accurately represented with these sources is a must. You can view a complete list of sources here: https://support.google.com/websearch/answer/2364942?p=medical_conditions&hl=en

For many US search results, Google seems favors a smaller number of government and commercial sites, including NiH.gov, FDA, as well as media sites like WebMD, RXList and MedInfo, major universities, and Wikipedia.

How to influence Knowledge Panels

It’s not possible to directly control the content or visibility of any individual knowledge panel. However there are some relatively easy ways to improve the odds that Google will include your content over another source.

First and most important is strong fundamentals. A fast-loading, error-free site allows Google’s bot to crawl and index the site. Make sure to enable Google Search Console and Google Analytics to get rich data on search traffic and impressions. Watch out for the typical SEO gotchas like content duplication or too much content locked up in images.

Next, implement rich snippets and structured data on your site. Many CMS systems have plugin support for schema.org definitions. Q&A Rich Snippets and Featured Snippets are easy starting points, and you can adapt existing FAQ-style pages to this format. When creating new pages on the site, focus on how new content can answer patient, caregiver and HCP questions.

Audit sites where your brand, product or drug facts are listed. If necessary, reach out to these sites to ensure that the content is accurate, and when appropriate, attributed back to your official site. Wikipedia and other self-service data sets are easy, quick fixes that often can generate an instant result.

Regular, authoritative content publishing is another way to address high demand search queries and improve the chances your content will be picked up vs. a competitor.

Finally, you can directly give feedback to Google regarding the validity and accuracy of their results. Each knowledge panel renders a small feedback link which you can use to submit feedback for review by Google.

Additional Reading

https://ahrefs.com/blog/search-traffic-study/

https://ahrefs.com/blog/find-featured-snippets/