Google has just released Bard, its answer to ChatGPT, and users are getting to know it to see how it compares to OpenAI’s artificial intelligence-powered chatbot.

The name ‘Bard’ is purely marketing-driven, as there are no algorithms named Bard, but we do know that the chatbot is powered by LaMDA.

Here is everything we know about Bard so far and some interesting research that may offer an idea of the kind of algorithms that may power Bard.

What Is Google Bard?

Bard is an experimental Google chatbot that is powered by the LaMDA large language model.

It’s a generative AI that accepts prompts and performs text-based tasks like providing answers and summaries and creating various forms of content.

Bard also assists in exploring topics by summarizing information found on the internet and providing links for exploring websites with more information.

Why Did Google Release Bard?

Google released Bard after the wildly successful launch of OpenAI’s ChatGPT, which created the perception that Google was falling behind technologically.

ChatGPT was perceived as a revolutionary technology with the potential to disrupt the search industry and shift the balance of power away from Google search and the lucrative search advertising business.

On December 21, 2022, three weeks after the launch of ChatGPT, the New York Times reported that Google had declared a “code red” to quickly define its response to the threat posed to its business model.

Forty-seven days after the code red strategy adjustment, Google announced the launch of Bard on February 6, 2023.

What Was The Issue With Google Bard?

The announcement of Bard was a stunning failure because the demo that was meant to showcase Google’s chatbot AI contained a factual error.

The inaccuracy of Google’s AI turned what was meant to be a triumphant return to form into a humbling pie in the face.

Google’s shares subsequently lost a hundred billion dollars in market value in a single day, reflecting a loss of confidence in Google’s ability to navigate the looming era of AI.

How Does Google Bard Work?

Bard is powered by a “lightweight” version of LaMDA.

LaMDA is a large language model that is trained on datasets consisting of public dialogue and web data.

There are two important factors related to the training described in the associated research paper, which you can download as a PDF here: LaMDA: Language Models for Dialog Applications (read the abstract here).

• A. Safety: The model achieves a level of safety by tuning it with data that was annotated by crowd workers.
• B. Groundedness: LaMDA grounds itself factually with external knowledge sources (through information retrieval, which is search).

The LaMDA research paper states:

“…factual grounding, involves enabling the model to consult external knowledge sources, such as an information retrieval system, a language translator, and a calculator.

We quantify factuality using a groundedness metric, and we find that our approach enables the model to generate responses grounded in known sources, rather than responses that merely sound plausible.”

Google used three metrics to evaluate the LaMDA outputs:

1. Sensibleness: A measurement of whether an answer makes sense or not.
2. Specificity: Measures if the answer is the opposite of generic/vague or contextually specific.
3. Interestingness: This metric measures if LaMDA’s answers are insightful or inspire curiosity.

All three metrics were judged by crowdsourced raters, and that data was fed back into the machine to keep improving it.

The LaMDA research paper concludes by stating that crowdsourced reviews and the system’s ability to fact-check with a search engine were useful techniques.

Google’s researchers wrote:

“We find that crowd-annotated data is an effective tool for driving significant additional gains.

We also find that calling external APIs (such as an information retrieval system) offers a path towards significantly improving groundedness, which we define as the extent to which a generated response contains claims that can be referenced and checked against a known source.”

How Is Google Planning To Use Bard In Search?

The future of Bard is currently envisioned as a feature in search.

Google’s announcement in February was insufficiently specific on how Bard would be implemented.

The key details were buried in a single paragraph close to the end of the blog announcement of Bard, where it was described as an AI feature in search.

That lack of clarity fueled the perception that Bard would be integrated into search, which was never the case.

Google’s February 2023 announcement of Bard states that Google will at some point integrate AI features into search:

“Soon, you’ll see AI-powered features in Search that distill complex information and multiple perspectives into easy-to-digest formats, so you can quickly understand the big picture and learn more from the web: whether that’s seeking out additional perspectives, like blogs from people who play both piano and guitar, or going deeper on a related topic, like steps to get started as a beginner.

These new AI features will begin rolling out on Google Search soon.”

It’s clear that Bard is not search. Rather, it is intended to be a feature in search and not a replacement for search.

What Is A Search Feature?

A feature is something like Google’s Knowledge Panel, which provides knowledge information about notable people, places, and things.

Google’s “How Search Works” webpage about features explains:

“Google’s search features ensure that you get the right information at the right time in the format that’s most useful to your query.

Sometimes it’s a webpage, and sometimes it’s real-world information like a map or inventory at a local store.”

In an internal meeting at Google (reported by CNBC), employees questioned the use of Bard in search.

One employee pointed out that large language models like ChatGPT and Bard are not fact-based sources of information.

The Google employee asked:

“Why do we think the big first application should be search, which at its heart is about finding true information?”

Jack Krawczyk, the product lead for Google Bard, answered:

“I just want to be very clear: Bard is not search.”

At the same internal event, Google’s Vice President of Engineering for Search, Elizabeth Reid, reiterated that Bard is not search.

She said:

“Bard is really separate from search…”

What we can confidently conclude is that Bard is not a new iteration of Google search. It is a feature.

Bard Is An Interactive Method For Exploring Topics

Google’s announcement of Bard was fairly explicit that Bard is not search. This means that, while search surfaces links to answers, Bard helps users investigate knowledge.

The announcement explains:

“When people think of Google, they often think of turning to us for quick factual answers, like ‘how many keys does a piano have?’

But increasingly, people are turning to Google for deeper insights and understanding – like, ‘is the piano or guitar easier to learn, and how much practice does each need?’

Learning about a topic like this can take a lot of effort to figure out what you really need to know, and people often want to explore a diverse range of opinions or perspectives.”

It may be helpful to think of Bard as an interactive method for accessing knowledge about topics.

Bard Samples Web Information

The problem with large language models is that they mimic answers, which can lead to factual errors.

The researchers who created LaMDA state that approaches like increasing the size of the model can help it gain more factual information.

But they noted that this approach fails in areas where facts are constantly changing during the course of time, which researchers refer to as the “temporal generalization problem.”

Freshness in the sense of timely information cannot be trained with a static language model.

The solution that LaMDA pursued was to query information retrieval systems. An information retrieval system is a search engine, so LaMDA checks search results.

This feature from LaMDA appears to be a feature of Bard.

The Google Bard announcement explains:

“Bard seeks to combine the breadth of the world’s knowledge with the power, intelligence, and creativity of our large language models.

It draws on information from the web to provide fresh, high-quality responses.”

LaMDA and (possibly by extension) Bard achieve this with what is called the toolset (TS).

The toolset is explained in the LaMDA researcher paper:

“We create a toolset (TS) that includes an information retrieval system, a calculator, and a translator.

TS takes a single string as input and outputs a list of one or more strings. Each tool in TS expects a string and returns a list of strings.

For example, the calculator takes “135+7721”, and outputs a list containing [“7856”]. Similarly, the translator can take “hello in French” and output [‘Bonjour’].

Finally, the information retrieval system can take ‘How old is Rafael Nadal?’, and output [‘Rafael Nadal / Age / 35’].

The information retrieval system is also capable of returning snippets of content from the open web, with their corresponding URLs.

The TS tries an input string on all of its tools, and produces a final output list of strings by concatenating the output lists from every tool in the following order: calculator, translator, and information retrieval system.

A tool will return an empty list of results if it can’t parse the input (e.g., the calculator cannot parse ‘How old is Rafael Nadal?’), and therefore does not contribute to the final output list.”

Here’s a Bard response with a snippet from the open web:

Screenshot of a Google Bard Chat, March 2023

Conversational Question-Answering Systems

There are no research papers that mention the name “Bard.”

However, there is quite a bit of recent research related to AI, including by scientists associated with LaMDA, that may have an impact on Bard.

The following doesn’t claim that Google is using these algorithms. We can’t say for certain that any of these technologies are used in Bard.

The value in knowing about these research papers is in knowing what is possible.

The following are algorithms relevant to AI-based question-answering systems.

One of the authors of LaMDA worked on a project that’s about creating training data for a conversational information retrieval system.

You can download the 2022 research paper as a PDF here: Dialog Inpainting: Turning Documents into Dialogs (and read the abstract here).

The problem with training a system like Bard is that question-and-answer datasets (like datasets comprised of questions and answers found on Reddit) are limited to how people on Reddit behave.

It doesn’t encompass how people outside of that environment behave and the kinds of questions they would ask, and what the correct answers to those questions would be.

The researchers explored creating a system read webpages, then used a “dialog inpainter” to predict what questions would be answered by any given passage within what the machine was reading.

A passage in a trustworthy Wikipedia webpage that says, “The sky is blue,” could be turned into the question, “What color is the sky?”

The researchers created their own dataset of questions and answers using Wikipedia and other webpages. They called the datasets WikiDialog and WebDialog.

• WikiDialog is a set of questions and answers derived from Wikipedia data.
• WebDialog is a dataset derived from webpage dialog on the internet.

These new datasets are 1,000 times larger than existing datasets. The importance of that is it gives conversational language models an opportunity to learn more.

The researchers reported that this new dataset helped to improve conversational question-answering systems by over 40%.

The research paper describes the success of this approach:

“Importantly, we find that our inpainted datasets are powerful sources of training data for ConvQA systems…

When used to pre-train standard retriever and reranker architectures, they advance state-of-the-art across three different ConvQA retrieval benchmarks (QRECC, OR-QUAC, TREC-CAST), delivering up to 40% relative gains on standard evaluation metrics…

Remarkably, we find that just pre-training on WikiDialog enables strong zero-shot retrieval performance—up to 95% of a finetuned retriever’s performance—without using any in-domain ConvQA data. “

Is it possible that Google Bard was trained using the WikiDialog and WebDialog datasets?

It’s difficult to imagine a scenario where Google would pass on training a conversational AI on a dataset that is over 1,000 times larger.

But we don’t know for certain because Google doesn’t often comment on its underlying technologies in detail, except on rare occasions like for Bard or LaMDA.

Large Language Models That Link To Sources

Google recently published an interesting research paper about a way to make large language models cite the sources for their information. The initial version of the paper was published in December 2022, and the second version was updated in February 2023.

This technology is referred to as experimental as of December 2022.

You can download the PDF of the paper here: Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models (read the Google abstract here).

The research paper states the intent of the technology:

“Large language models (LLMs) have shown impressive results while requiring little or no direct supervision.

Further, there is mounting evidence that LLMs may have potential in information-seeking scenarios.

We believe the ability of an LLM to attribute the text that it generates is likely to be crucial in this setting.

We formulate and study Attributed QA as a key first step in the development of attributed LLMs.

We propose a reproducible evaluation framework for the task and benchmark a broad set of architectures.

We take human annotations as a gold standard and show that a correlated automatic metric is suitable for development.

Our experimental work gives concrete answers to two key questions (How to measure attribution?, and How well do current state-of-the-art methods perform on attribution?), and give some hints as to how to address a third (How to build LLMs with attribution?).”

This kind of large language model can train a system that can answer with supporting documentation that, theoretically, assures that the response is based on something.

The research paper explains:

“To explore these questions, we propose Attributed Question Answering (QA). In our formulation, the input to the model/system is a question, and the output is an (answer, attribution) pair where answer is an answer string, and attribution is a pointer into a fixed corpus, e.g., of paragraphs.

The returned attribution should give supporting evidence for the answer.”

This technology is specifically for question-answering tasks.

The goal is to create better answers – something that Google would understandably want for Bard.

• Attribution allows users and developers to assess the “trustworthiness and nuance” of the answers.
• Attribution allows developers to quickly review the quality of the answers since the sources are provided.

One interesting note is a new technology called AutoAIS that strongly correlates with human raters.

In other words, this technology can automate the work of human raters and scale the process of rating the answers given by a large language model (like Bard).

The researchers share:

“We consider human rating to be the gold standard for system evaluation, but find that AutoAIS correlates well with human judgment at the system level, offering promise as a development metric where human rating is infeasible, or even as a noisy training signal. “

This technology is experimental; it’s probably not in use. But it does show one of the directions that Google is exploring for producing trustworthy answers.

Research Paper On Editing Responses For Factuality

Lastly, there’s a remarkable technology developed at Cornell University (also dating from the end of 2022) that explores a different way to source attribution for what a large language model outputs and can even edit an answer to correct itself.

Cornell University (like Stanford University) licenses technology related to search and other areas, earning millions of dollars per year.

It’s good to keep up with university research because it shows what is possible and what is cutting-edge.

You can download a PDF of the paper here: RARR: Researching and Revising What Language Models Say, Using Language Models (and read the abstract here).

The abstract explains the technology:

“Language models (LMs) now excel at many tasks such as few-shot learning, question answering, reasoning, and dialog.

However, they sometimes generate unsupported or misleading content.

A user cannot easily determine whether their outputs are trustworthy or not, because most LMs do not have any built-in mechanism for attribution to external evidence.

To enable attribution while still preserving all the powerful advantages of recent generation models, we propose RARR (Retrofit Attribution using Research and Revision), a system that 1) automatically finds attribution for the output of any text generation model and 2) post-edits the output to fix unsupported content while preserving the original output as much as possible.

…we find that RARR significantly improves attribution while otherwise preserving the original input to a much greater degree than previously explored edit models.

Furthermore, the implementation of RARR requires only a handful of training examples, a large language model, and standard web search.”

How Do I Get Access To Google Bard?

Google is currently accepting new users to test Bard, which is currently labeled as experimental. Google is rolling out access for Bard here.

Screenshot from bard.google.com, March 2023

Google is on the record saying that Bard is not search, which should reassure those who feel anxiety about the dawn of AI.

We are at a turning point that is unlike any we’ve seen in, perhaps, a decade.

Understanding Bard is helpful to anyone who publishes on the web or practices SEO because it’s helpful to know the limits of what is possible and the future of what can be achieved.

More Resources:

Featured Image: Whyredphotographor/Shutterstock

Mozilla announced the founding of an open source initiative for developing AI that puts transparency, accountability and trustworthiness at the forefront of the open source AI products they will build to challenge Microsoft, OpenAI and Google.

Many of the most important software products that make modern life possible, like Android, WordPress, PHP, Nginx and Apache are all open source.

If successful, Mozilla will be at the forefront of changing what the future of AI will look like.

By creating a company that actively creates free and open source AI, they are taking a more active role in challenging big technology companies like OpenAI and Google.

Mozilla pledged $30 million dollars for developing an alternative to the closed systems developed by big technology companies that put profits first.

The Mozilla approach, like everything else they build, will be human-first.

Their new website, Mozilla.ai explains:

“Mozilla has long championed a world where AI is more trustworthy, investing in startups, advocating for laws, and mobilizing the public to focus on human agency and accountability in AI.

Now we’re taking the next step: creating a company — and gathering a community — to build a trustworthy open source AI ecosystem.”

Mozilla and the Goals for Open Source AI

Mozilla is a non-profit organization that builds browsers, email clients, a VPN, email privacy apps, and other products that are free, open-source, and privacy-first.

The Mozilla mission statement says that they are focused on creating an Internet that is people-first, open, free and accessible.

Mozilla is committed to helping create an Internet that hews to the following principles:

• “The internet is a global public resource that must remain open and accessible
• Individuals’ security and privacy on the internet are fundamental and must not be treated as optional.
• Individuals must have the ability to shape the internet and their own experiences on it.
• Free and open source software promotes the development of the internet as a public resource.
• Transparent community-based processes promote participation, accountability and trust.
• Magnifying the public benefit aspects of the internet is an important goal, worthy of time, attention and commitment.”

Decentralized and Open Source AI

Mozilla stated that they intend to create a decentralized AI community that can serve as a “counterweight” against the large profit-focused companies.

When it comes to something like AI, it makes sense that the future of the technology is created by people who are focused on benefiting humanity as opposed to commodifying humans.

The announcement stated that their first two projects will focus on creating a free, trustworthy and open source generative AI, which is a product like ChatGPT and Bard.

Their second focus is on creating a privacy-first recommendation system that “don’t misinform or undermine our well-being.”

These kinds of products can be one be used by companies to build into their apps and devices so that their users can be assured about the products.

Read Mozilla’s announcement here:

Introducing Mozilla.ai: Investing in trustworthy AI

Featured image by Shutterstock/Roman Samborskyi

Marketing is one of the most important skills to learn as an e-commerce store owner. By learning marketing, you’ll always have a steady stream of new customers.

Plus, knowing the basics of marketing can get you ahead of the competition, and it’s valuable to have a base understanding if you ever hire marketing roles for your company.

In this guide, I share the five main marketing channels and how to use them, plus a few marketing tips to help you earn more and spend less.

There are five main channels you can use to promote your products. They are:

1. Search engines
2. Social media sites
3. Email inboxes
4. Display ads
5. Brand affiliates

Let’s talk about how you can use each of these channels in your e-commerce marketing plan.

1. Search engine marketing (SEM)

Search engine marketing covers both organic and paid traffic from search engines like Google. 

Both are important. Take Solo Stove, for example. Its online store gets over 300,000 organic visits from Google every month—plus an additional ~28,000 monthly visits from paid ads:

Search engine optimization (SEO)

In order to show up organically on the first page of Google’s search results, you need to learn and implement search engine optimization practices on your website. 

This includes things like:

• Figuring out what keywords people are searching for to find your products.
• Aligning with the search intent of the query.
• Getting other websites to link to your website (aka backlinks).
• And more.

I’ll discuss these steps in more detail in the “tips” section below. For now, if you want to learn more, check out our complete guide to e-commerce SEO.

Paid search ads

You can pay to “skip the line” and show up at the top of Google’s search results. This is called pay-per-click (PPC) advertising, and it’s a great complement to your SEO efforts. PPC ads are a quick and easy (albeit sometimes expensive) way to get in front of your target audience.

Here’s a chart explaining why you should utilize both PPC and SEO:

What does this look like? You’ve probably seen ads like these, annotated with the word “Sponsored” next to them:

You can run Google Ads by creating an account, choosing the page you want to send visitors to, writing up various headlines and description ad copy, and selecting keywords to be displayed for. 

But there’s quite a bit more to it than that—it takes time and money to learn what works. Check out our guide to Google Ads basics to get started.

2. Social media marketing

Probably the most obvious place to market your e-commerce store is on the many social media apps. 

Again, with Solo Stove as an example—it uses both organic and paid social media marketing and has been able to gain over half a million TikTok followers, 347,000 Instagram followers, and almost 300,000 Facebook followers.

Let’s take a look at how you can do the same:

Organic social media marketing

Growing an organic following on social media is a great way to get your brand and products in front of people without spending a ton of money. However, it’s also a lot of work—especially if you plan on growing multiple channels.

If you’re not sure which channel(s) to use, a good starting point is SparkToro. You can type in a product keyword like “mens boots,” and it’ll show you social stats of relevant accounts:

From here, if you hover over the social media icons, you can see the individual channel statistics. This tells you which channels brands have the most followers on, which can be a hint on which channels are most effective for them.

Use this data to decide which channels you should invest your time in first. From there, check out this list of resources to learn more about how to grow your accounts.

Paid social media advertising

The other side of the coin is social media PPC ads. You can use ads to drive immediate sales—but at a cost. There’s a steep learning curve to maximizing sales while minimizing ad costs.

That said, one of the easiest ways to run a successful social media ad is through retargeting customers who abandon carts. This works by putting a browser cookie on a visitor who adds an item to their cart but doesn’t check out, then using that cookie to show them ads on social media of the item they left in their cart.

Again, Solo Stove does this well. I added this heat deflector to my cart…

… then almost immediately saw this ad on my Facebook feed after leaving its site without buying:

There’s a lot more you can do with these ads, though. Check out Mayple’s guide to social media advertising to learn more.

3. Email marketing

Email newsletters are typically one of the highest-converting traffic sources for e-commerce stores. This is because your email list, if done well, will be full of people who know who you are and have an active interest in your brand. That said, you need traffic to grow an email list, so it doesn’t make a good stand-alone marketing channel.

There are many ways to grow an email list, including:

• Email opt-in forms on your site offering a discount or free information.
• Collecting your customer’s emails when they make a purchase (with their permission, of course).
• Running a giveaway for your products.

Once you have an email list, you can send them product updates, content from your blog, clearance sales, etc. 

Here’s an example from clothing brand Off The Grid, which uses its newsletter to give tips on how to get the most out of its clothes:

Just make sure you keep your list engaged by deleting inactive subscribers every three to six months and avoid sending too many emails. Your list is one of your biggest assets, so take care of it.

4. Display ads

Have you ever been bombarded by display ads on every website you visit after looking at an online store but leaving without buying anything? 

This is because the online store you visited placed a cookie in your browser that allowed it to “retarget” you with display ads across any websites that run these retargeting ads. What I already showed in the “social media ads” section above was a retargeting ad too.

It’s been found that it takes anywhere from 28–62 (or more) “touchpoints” to make a sale.

A “touchpoint” is any time a potential customer is shown a brand, either through an ad or by visiting your website or social media channel. Every time they see your brand or product, that’s one touchpoint.

That’s what makes these retargeting ads so effective. You can get multiple touchpoints of your product at a relatively low price compared to traditional PPC ads.

The catch is that you can only show retargeting ads to people who have either visited your website and allowed the cookie in their browser settings, or to people in your email list.

HubSpot has an excellent beginner’s guide to retargeting if you want to learn more.

You can also run general display ads, which are suitable for making people aware of your brand and products. You can use them to get people to your site, then run retargeting ads to those people who visited your initial ad but didn’t purchase.

For example, Advance Auto Parts paid to show me these display ads across various blogs even though I haven’t visited its site before:

Check out Google Display Ads if that’s something you’re interested in.

5. Affiliate marketing

Affiliate marketing is where someone promotes your product or service and makes a commission any time they send you a sale. 

This typically works by giving your affiliate a unique ID that they include in their URL when they link to your website. It might look like this:

https://www.yourdomain.com/your-product?ref=UniqueAffiliateID

When a customer makes a sale through the URL with the unique affiliate ID attached, your affiliate program will attribute that sale to that particular affiliate so you can pay them their percent of the income.

For example, Solo Stove has an affiliate program, and I used to promote it in my articles and videos, like this blog post and YouTube video review: 

To learn more about setting up an affiliate program for your e-commerce store, see this guide.

Now that you know where to promote your products, here are a few tips to help you maximize your sales and minimize your marketing costs:

1. Don’t compete solely on price

Above all, never get into a price war. You will never be able to compete with giant brands on price. They can afford to lose money until you’re long out of business.

Instead, compete on things like quality, customer service, experience, and value. 

Make sure the entire experience of finding your brand and buying from you is seamless and easy. And use your marketing to educate and entertain, not just to promote your product. If you offer people something of value first, they will be more likely to buy, even at a higher price point.

For example, Squatty Potty both informs and entertains in what is arguably one of the best ads ever made:

Or, back to Solo Stove, it makes videos that teach you the best way to use its products:

2. Don’t offer big discounts

Offering discounts may be an easy way to make a quick buck. But in doing so, you may be shooting yourself in the foot. By offering frequent discounts, people may come to expect your discounts and won’t buy your products at full price because they know discounts are coming.

3. Begin with keyword research

Search engines can be a lucrative source of free marketing if you’re able to rank highly on them. But SEO can take years—especially for a beginner.

That’s why it’s best to do some keyword research to figure out what your customers are searching for so you can start optimizing your site right away. (You’ll thank me in two years.)

You can do this with Ahrefs’ free keyword generator tool. Type in a broad keyword that describes your products, and the tool will spit out keyword ideas with some basic data:

Keep in mind that you’ll want to find different keywords for different purposes. 

For example, “brown leather boots” may be a good keyword for your category page, while your product pages may be better served with more specific keywords like “brown leather ugg boots” or “womens brown leather knee high boots.” 

Basically, use broad keywords for category pages and specific keywords for product pages. Again, refer to our e-commerce SEO guide to learn more.

4. Optimize your website for search and conversions

Continuing from the last tip, you should take the keyword research you did and optimize your category and product pages for their best keywords. 

This is called on-page SEO, and it involves:

• Talking about your target keyword in your title, URL, and within the page itself.
• Writing a compelling title tag and description to make your result stand out on the SERPs.
• Optimizing your images to load fast and have descriptive filenames and alt text.
• Including internal links between your pages to make them easy to find.

There’s a bit more that goes into it, so read our on-page SEO guide to learn more.

Beyond SEO, you should also optimize your website for conversions. After all, you don’t want to spend all this time and money on marketing only to lose sales, right?

Conversion rate optimization (CRO) includes things like using high-quality images, effective copywriting, and clean website design with minimal distractions. I highly recommend going through Shopify’s CRO guide.

5. Start a blog

Having a great product and effective ads can only take you so far. If you want to utilize organic marketing channels like social media, search engines, and newsletters, you need to offer more than just advertisements for your products.

That’s where content comes in.

Photos, videos, and blog posts give you the ability to capture customers at different stages in the marketing funnel whom you otherwise wouldn’t have sold to.

Here’s what this may look like and what people may search for at each stage:

Let’s say you own a shoe store. A potential customer has a problem; they need a good pair of waterproof shoes that are functional but also look good. So they do a Google search for “stylish mens waterproof shoes” (the “service or product” stage).

The results aren’t shoe stores. They’re all blogs that talk about shoes:

It’s possible for you to write a blog post of your own with the goal of ranking well for that keyword and promoting your own shoes. You can also use that article as content to add to your email newsletter and social media feeds.

Pro Tip

It will also be a great idea to reach out to all the blogs that are ranking to try and get them to include your shoes as well. If you start an affiliate program, you can tell them about it, and they’ll be more likely to include your products since they have an incentive.

Expand this idea for other issues potential customers may have, like learning different ways to tie shoes or ideas for outfits that can go with your shoes. You’re only limited by your creativity.

Check out my guide to e-commerce blogging to learn more.

6. Create video content

Video content is becoming more and more important. If you want to do well on TikTok, Instagram, YouTube, and even Facebook, you need to make videos. Plus, many of the SERPs now contain video results in addition to blog pages.

For example, we manage to rank for the keyword “learn seo” with both a blog post and a YouTube video:

We wrote a full guide to video SEO if you want to learn how we did it.

But what kind of videos should you create? 

It depends on your audience, the platform, and your product. In general, shorter videos do better than long ones, like this five-second TikTok by Guess that got over 800,000 views:

Of course, there are a lot of ways to utilize video in your marketing plan, and there’s plenty of space for longer videos. Check out our guide to video marketing to learn more.

7. Make standard operating procedures

Many of your marketing tasks will be repeatable. Things like outlining your content, sharing your posts, and even running ads can all be standardized to make things quicker and easier.

This is why you should create standard operating procedures for these tasks. A standard operating procedure (SOP) is a document that outlines exactly how to do a task step by step—often with screenshots or videos—that allows you to hand off the task to a virtual assistant to free up your time and streamline the process.

For example, we have a guide to creating SEO SOPs. But you can make an SOP for any repeatable task, such as: 

• Creating blog or product images.
• Adding new products to your newsletter and social media feeds.
• And so much more.

Here’s an example of a step in our SOP for creating content at Ahrefs:

Creating an SOP is easy. Just create a Google Doc and use headings to organize your task into steps and add screenshots or even videos to show the process. The clearer and more concise you can be, the better. 

Final thoughts

Learning e-commerce marketing is a surefire way to make a lot more money from your online store. There’s a lot to learn, so take it one thing at a time.

Eventually, you should aim to hire a VA or marketing team to help with these tasks so you can focus on other areas of your business. Having a basic understanding of how they’re done will help you make good hiring decisions.