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6 min read Leveraging Artificial Intelligence in Your Marketing Strategy

Leveraging Artificial Intelligence in Your Marketing Strategy

Key Takeaways

  • AI can help businesses with a wide range of tasks, from content creation to customer service.
  • AI tools are only as accurate as the data given to them. So, businesses should ensure their datasets are extensive before handing them over to AI models.
  • Businesses using AI should be mindful of privacy issues, transparency with customers, and over-reliance.

Every­one’s talk­ing about arti­fi­cial intel­li­gence (AI)! Want to know how it can ben­e­fit your busi­ness’s mar­ket­ing strat­e­gy? Let’s dive into the main ways you can utilise AI:

Customer service

Though cus­tomer ser­vice won’t be the first thought to pop into your head when think­ing about your mar­ket­ing strat­e­gy, it’s a cru­cial part of cus­tomer rela­tion­ship man­age­ment (CRM) and nur­tur­ing leads. Mag­net­ic mar­ket­ing might be what draws in the leads, but qual­i­ty cus­tomer ser­vice is what keeps them com­ing back.

In the realm of cus­tomer ser­vice, AI has made sig­nif­i­cant strides, most notably through chat­bots. These AI-dri­ven vir­tu­al assis­tants are pro­grammed to han­dle a myr­i­ad of cus­tomer inquiries, from clar­i­fy­ing prod­uct details to trou­bleshoot­ing tech­ni­cal issues.

Their abil­i­ty to pro­vide instant respons­es 24/7 caters to the mod­ern con­sumer’s need for imme­di­ate grat­i­fi­ca­tion, but they do have some draw­backs. The way in which busi­ness­es design their chat­bots can have both pos­i­tive and neg­a­tive effects on site vis­i­tors, KPIs, and con­ver­sion rates. So, it’s impor­tant to get them right.

With that in mind, con­sid­er the fol­low­ing pros and cons of using chatbots:


  • Effi­cien­cy: Chat­bots can han­dle mul­ti­ple queries simul­ta­ne­ous­ly, reduc­ing wait times.
  • Avail­abil­i­ty: They oper­ate round-the-clock, ensur­ing cus­tomers in dif­fer­ent time zones are catered to.
  • Data col­lec­tion: Every inter­ac­tion pro­vides busi­ness­es with valu­able data, offer­ing insights into cus­tomer pref­er­ences and pain points.


  • Lim­it­ed under­stand­ing: Chat­bots can some­times mis­in­ter­pret com­plex queries, lead­ing to frus­tra­tion in customers.
  • Imper­son­al inter­ac­tion: Some cus­tomers still pre­fer the human touch, feel­ing dis­con­nect­ed from web­sites that use chatbots.

Clear­ly, inte­grat­ing AI into cus­tomer ser­vice offers sev­er­al ben­e­fits to busi­ness­es, but it should be done with care. When cre­at­ing your chat­bot con­ver­sa­tion flows, remem­ber that a high­er num­ber of flows will allow it to deal with a wider range of queries. Plus, you should allow for instances where the chat­bot won’t be able to help – how quick­ly can a mem­ber of your team respond to the customer?


The age of one-size-fits-all mar­ket­ing is wan­ing, replaced by the era of per­son­al­i­sa­tion, and AI is at the fore­front of this transformation.

By analysing user behav­iour, pur­chase his­to­ry, and even brows­ing pat­terns, AI algo­rithms can curate expe­ri­ences tai­lored to indi­vid­ual pref­er­ences. This can enhance engage­ment rates, whether it’s prod­uct rec­om­men­da­tions or tar­get­ed emails. Fur­ther­more, busi­ness­es can ensure their mar­ket­ing bud­get is spent on gen­uine­ly inter­est­ed con­sumers by tar­get­ing users more precisely.

The third ben­e­fit, and per­haps the most impor­tant, is that cus­tomers feel val­ued when brands pro­vide tai­lored expe­ri­ences, fos­ter­ing loy­al­ty and encour­ag­ing repeat business.

So, how can your busi­ness inte­grate AI into its per­son­al­i­sa­tion efforts? Let’s dive into per­son­al­is­ing email marketing:

Personalisation in email marketing

In the vast realm of dig­i­tal mar­ket­ing, email remains a pow­er­ful tool, and with the inte­gra­tion of AI, its poten­tial is bound­less. AI trans­forms email mar­ket­ing from a sta­t­ic com­mu­ni­ca­tion chan­nel into a dynam­ic, respon­sive, and intu­itive platform.

By analysing user behav­iours, open rates, and click-through pat­terns, AI can deter­mine the opti­mal times to send emails, ensur­ing they land in inbox­es when recip­i­ents are most like­ly to engage.

Addi­tion­al­ly, AI-dri­ven automa­tion tools can seg­ment email lists with pre­ci­sion, deliv­er­ing tai­lored con­tent to spe­cif­ic audi­ence sub­sets and enhanc­ing rel­e­vance and engagement.

In a world where emails evolve based on user inter­ac­tions, a sub­scriber can show inter­est in a par­tic­u­lar prod­uct cat­e­go­ry, and AI can ensure they receive con­tent aligned with that inter­est in next to real-time.

Fur­ther­more, AI can craft sub­ject lines that cap­ti­vate, test vari­a­tions of email designs to see which res­onates best, and even pre­dict future trends in sub­scriber behaviour.

Warnings when using AI to create personalised experiences

Despite the many excit­ing oppor­tu­ni­ties AI pos­es, busi­ness­es should be aware of the issue of pri­va­cy. Per­son­al­i­sa­tion requires data and mis­han­dling or breach­es can lead to sig­nif­i­cant trust issues from customers.

Fur­ther­more, there is such a thing as over-per­son­al­i­sa­tion. There’s a fine line between tai­lored and intru­sive. Over-per­son­al­i­sa­tion can make cus­tomers feel over­ly mon­i­tored, lead­ing to dis­com­fort, and they may unsub­scribe or stop inter­act­ing with your busi­ness completely.

Content creation

With con­tent a high pri­or­i­ty for many busi­ness­es, AI is an attrac­tive solu­tion to the time and cost con­straints that can reduce con­tent out­put. From gen­er­at­ing draft arti­cles, brain­storm­ing ideas, and writ­ing emails and social media posts, AI tools har­ness vast data to pro­duce con­tent that res­onates with audiences.

Though a human is always required to edit AI-gen­er­at­ed con­tent, using a tool such as Chat­G­PT to write an ini­tial draft can great­ly reduce the time spent writ­ing and researching.

Before jump­ing on this trend and fir­ing your writ­ers, let’s dive into the main con­sid­er­a­tions when using AI to cre­ate content:

  • AI can gen­er­ate large vol­umes of con­tent in a frac­tion of the time it would take humans, mak­ing it invalu­able for busi­ness­es with exten­sive con­tent needs. How­ev­er, it may lack the gen­uine human touch, emo­tion, and nuance that res­onate deeply with audiences.
  • Since AI analy­ses user inter­ac­tions, it can ensure con­tent is aligned with audi­ence pref­er­ences and cur­rent trends. Despite this, it should­n’t be relied upon for pro­vid­ing sol­id facts and fig­ures (these should always be mon­i­tored by a human).
  • Beyond text, AI can assist in video pro­duc­tion, image selec­tion, and even audio con­tent, ensur­ing a cohe­sive mul­ti­me­dia strat­e­gy. How­ev­er, sole­ly depend­ing on AI can lead to homogenised con­tent, miss­ing out on unique human per­spec­tives and oppor­tu­ni­ties for creativity.

Giv­en that AI-gen­er­at­ed con­tent comes with so many highs and lows, we rec­om­mend a blend­ed approach. Busi­ness­es should make sure AI works for them, not instead of them, since, at this moment in time, AI con­tent is sim­ply not good enough to be left unedited.

Data interpretation

In the dig­i­tal age, busi­ness­es are inun­dat­ed with data. From cus­tomer inter­ac­tions and sales met­rics to social media engage­ment and web­site ana­lyt­ics, the sheer vol­ume of infor­ma­tion can be overwhelming.

How­ev­er, this data, when inter­pret­ed cor­rect­ly, holds the key to trans­for­ma­tive busi­ness insights.

AI sys­tems are designed to sift through vast datasets, iden­ti­fy­ing pat­terns and cor­re­la­tions that might elude the human eye. For mar­keters, this means a clear­er under­stand­ing of cus­tomer behav­iours, pref­er­ences, and pain points. Instead of broad-stroke cam­paigns, mar­ket­ing strate­gies can be fine-tuned to address spe­cif­ic audi­ence seg­ments, max­imis­ing engage­ment and ROI.

Beyond mar­ket­ing, data inter­pre­ta­tion pow­ered by AI has broad­er busi­ness implications.

Teams can lever­age AI to do the following:

  • Fore­cast rev­enue streams
  • Iden­ti­fy poten­tial cost-sav­ing areas
  • Pre­dict mar­ket fluctuations
  • Opti­mise sup­ply chains
  • Enhance prod­uct quality
  • Stream­line processes

How­ev­er, the true mag­ic of AI in data inter­pre­ta­tion lies in its pre­dic­tive capa­bil­i­ties. Rather than just under­stand­ing the present land­scape, busi­ness­es can antic­i­pate future trends, allow­ing them to stay ahead of the curve and adapt to chang­ing mar­ket dynam­ics proactively.

Predictive analysis

What is it?

Pre­dic­tive analy­sis, pow­ered by AI, involves using his­tor­i­cal data to fore­cast future out­comes. In the con­text of mar­ket­ing, it’s about antic­i­pat­ing cus­tomer behav­iours, sales trends, and mar­ket shifts before they happen.

Why is it important?

In the mar­ket­ing world, being one step ahead is invalu­able. Pre­dic­tive analy­sis offers mar­keters a crys­tal ball, allow­ing them to tai­lor strate­gies based on future pre­dic­tions rather than past or cur­rent trends. This proac­tive approach ensures opti­mal resource allo­ca­tion, max­imis­es ROI, and enhances cus­tomer engagement.

How is it implemented in a marketing strategy?

1. Gain­ing cus­tomer insights

By analysing past pur­chase behav­iours, pre­dic­tive mod­els can iden­ti­fy which cus­tomers are most like­ly to make a repeat pur­chase. Tai­lored cam­paigns can then be designed to tar­get these spe­cif­ic segments.

2. Fore­cast­ing sales

Pre­dic­tive analy­sis can gauge future sales vol­umes, help­ing busi­ness­es man­age inven­to­ry, opti­mise pric­ing strate­gies, and allo­cate mar­ket­ing bud­gets effectively.

3. Opti­mis­ing ads

By pre­dict­ing which ad cam­paigns or chan­nels will res­onate most with audi­ences, mar­keters can ensure their adver­tis­ing spend yields the high­est returns.

4. Con­tent personalisation

Pre­dic­tive mod­els can fore­cast what type of con­tent a user is like­ly to engage with next, allow­ing for real-time con­tent rec­om­men­da­tions that boost engagement.

5. Mar­ket trend analysis

Beyond indi­vid­ual con­sumer behav­iours, pre­dic­tive analy­sis can also fore­cast broad­er mar­ket trends, help­ing brands stay ahead of the curve and adjust their strate­gies accordingly.

A word of warning

While pre­dic­tive analy­sis builds on stur­dy datasets, it’s essen­tial to approach its results with a degree of cau­tion. Sud­den mar­ket shifts or unprece­dent­ed events can sig­nif­i­cant­ly impact pre­dic­tions. So, it’s cru­cial for busi­ness­es to use pre­dic­tive analy­sis as a guid­ing tool rather than an absolute determinant.

Best practices for using AI:

Giv­en that AI is rel­a­tive­ly new, there are lots of things we don’t under­stand about it yet. Make sure you keep the fol­low­ing in mind so that you’re using AI safe­ly and effectively:

  • AI mod­els are only as good as the data they’re trained on. So, ensure the data you feed your AI tools is high qual­i­ty and accurate.
  • Cus­tomers val­ue trans­paren­cy. So, be open about how and where you’re using AI, espe­cial­ly when inter­act­ing with customers.
  • AI isn’t a set-it-and-for­get-it tool. Reg­u­lar­ly update and train your mod­els to ensure they remain accu­rate and effective.
  • Ensure that your AI tools inte­grate seam­less­ly with oth­er sys­tems and plat­forms your busi­ness uses.
  • AI mod­els may still per­pet­u­ate or ampli­fy bias­es present in their train­ing data. So, this is some­thing you’ll need to mit­i­gate against.
  • When pro­vid­ing AI tools with cus­tomer data, you need to respect user pri­va­cy in accor­dance with local and up-to-date laws and customs.

What might AI have in store for the future?

Though AI’s cur­rent capa­bil­i­ties are enough to be get­ting on with, we can’t help but won­der what excit­ing things it might be able to offer us in the future. As far as we know, the fol­low­ing may be pos­si­ble in the next ten years:

  • Emo­tion recog­ni­tion for respon­sive advertising
  • Aug­ment­ed real­i­ty that allows users to try prod­ucts before they buy them
  • Real-time lan­guage trans­la­tions for inter­na­tion­al business
  • Neu­ro-mar­ket­ing
  • Inte­gra­tion with the Inter­net of Things (IoT)

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